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Sample records for iterative learning controller

  1. An iterative learning controller for nonholonomic mobile robots

    SciTech Connect

    Oriolo, G.; Panzieri, S.; Ulivi, G.

    1998-09-01

    The authors present an iterative learning controller that applies to nonholonomic mobile robots, as well as other systems that can be put in chained form. The learning algorithm exploits the fact that chained-form. The learning algorithm exploits the fact that chained-form systems are linear under piecewise-constant inputs. The proposed control scheme requires the execution of a small number of experiments to drive the system to the desired state in finite time, with nice convergence and robustness properties with respect to modeling inaccuracies as well as disturbances. To avoid the necessity of exactly reinitializing the system at each iteration, the basic method is modified so as to obtain a cyclic controller, by which the system is cyclically steered through an arbitrary sequence of states. As a case study, a carlike mobile robot is considered. Both simulation and experimental results are reported to show the performance of the method.

  2. Iterative learning control algorithm for spiking behavior of neuron model

    NASA Astrophysics Data System (ADS)

    Li, Shunan; Li, Donghui; Wang, Jiang; Yu, Haitao

    2016-11-01

    Controlling neurons to generate a desired or normal spiking behavior is the fundamental building block of the treatment of many neurologic diseases. The objective of this work is to develop a novel control method-closed-loop proportional integral (PI)-type iterative learning control (ILC) algorithm to control the spiking behavior in model neurons. In order to verify the feasibility and effectiveness of the proposed method, two single-compartment standard models of different neuronal excitability are specifically considered: Hodgkin-Huxley (HH) model for class 1 neural excitability and Morris-Lecar (ML) model for class 2 neural excitability. ILC has remarkable advantages for the repetitive processes in nature. To further highlight the superiority of the proposed method, the performances of the iterative learning controller are compared to those of classical PI controller. Either in the classical PI control or in the PI control combined with ILC, appropriate background noises are added in neuron models to approach the problem under more realistic biophysical conditions. Simulation results show that the controller performances are more favorable when ILC is considered, no matter which neuronal excitability the neuron belongs to and no matter what kind of firing pattern the desired trajectory belongs to. The error between real and desired output is much smaller under ILC control signal, which suggests ILC of neuron’s spiking behavior is more accurate.

  3. Fast calculation of the `ILC norm' in iterative learning control

    NASA Astrophysics Data System (ADS)

    Rice, Justin K.; van Wingerden, Jan-Willem

    2013-06-01

    In this paper, we discuss and demonstrate a method for the exploitation of matrix structure in computations for iterative learning control (ILC). In Barton, Bristow, and Alleyne [International Journal of Control, 83(2), 1-8 (2010)], a special insight into the structure of the lifted convolution matrices involved in ILC is used along with a modified Lanczos method to achieve very fast computational bounds on the learning convergence, by calculating the 'ILC norm' in ? computational complexity. In this paper, we show how their method is equivalent to a special instance of the sequentially semi-separable (SSS) matrix arithmetic, and thus can be extended to many other computations in ILC, and specialised in some cases to even faster methods. Our SSS-based methodology will be demonstrated on two examples: a linear time-varying example resulting in the same ? complexity as in Barton et al., and a linear time-invariant example where our approach reduces the computational complexity to ?, thus decreasing the computation time, for an example, from the literature by a factor of almost 100. This improvement is achieved by transforming the norm computation via a linear matrix inequality into a check of positive definiteness - which allows us to further exploit the almost-Toeplitz properties of the matrix, and additionally provides explicit upper and lower bounds on the norm of the matrix, instead of the indirect Ritz estimate. These methods are now implemented in a MATLAB toolbox, freely available on the Internet.

  4. Multivariable norm optimal iterative learning control with auxiliary optimisation

    NASA Astrophysics Data System (ADS)

    Owens, David H.; Freeman, Chris T.; Chu, Bing

    2013-06-01

    The paper describes a substantial extension of norm optimal iterative learning control (NOILC) that permits tracking of a class of finite dimensional reference signals whilst simultaneously converging to the solution of a constrained quadratic optimisation problem. The theory is presented in a general functional analytical framework using operators between chosen real Hilbert spaces. This is applied to solve problems in continuous time where tracking is only required at selected intermediate points of the time interval but, simultaneously, the solution is required to minimise a specified quadratic objective function of the input signals and chosen auxiliary (state) variables. Applications to the discrete time case, including the case of multi-rate sampling, are also summarised. The algorithms are motivated by practical need and provide a methodology for reducing undesirable effects such as payload spillage, vibration tendencies and actuator wear whilst maintaining the desired tracking accuracy necessary for task completion. Solutions in terms of NOILC methodologies involving both feedforward and feedback components offer the possibilities of greater robustness than purely feedforward actions. Results describing the inherent robustness of the feedforward implementation are presented and the work is illustrated by experimental results from a robotic manipulator.

  5. H∞ iterative learning controller design for a class of discrete-time systems with data dropouts

    NASA Astrophysics Data System (ADS)

    Bu, Xuhui; Hou, Zhongsheng; Yu, Fashan; Wang, Fuzhong

    2014-09-01

    In this paper, the issue of H∞ iterative learning controller design is considered for a class of discrete-time systems with data dropouts. With the super-vector formulation of iterative learning control (ILC), such a system can be formulated as a linear discrete-time stochastic system in the iteration domain, and then a sufficient condition guaranteeing both stability of the ILC process and the desired H∞ performance in the iteration domain is presented. The condition can be derived in terms of linear matrix inequalities that can be solved by using existing numerical techniques. A numerical simulation example is also included to validate the theoretical results.

  6. Direct adaptive iterative learning control of nonlinear systems using an output-recurrent fuzzy neural network.

    PubMed

    Wang, Ying-Chung; Chien, Chiang-Ju; Teng, Ching-Cheng

    2004-06-01

    In this paper, a direct adaptive iterative learning control (DAILC) based on a new output-recurrent fuzzy neural network (ORFNN) is presented for a class of repeatable nonlinear systems with unknown nonlinearities and variable initial resetting errors. In order to overcome the design difficulty due to initial state errors at the beginning of each iteration, a concept of time-varying boundary layer is employed to construct an error equation. The learning controller is then designed by using the given ORFNN to approximate an optimal equivalent controller. Some auxiliary control components are applied to eliminate approximation error and ensure learning convergence. Since the optimal ORFNN parameters for a best approximation are generally unavailable, an adaptive algorithm with projection mechanism is derived to update all the consequent, premise, and recurrent parameters during iteration processes. Only one network is required to design the ORFNN-based DAILC and the plant nonlinearities, especially the nonlinear input gain, are allowed to be totally unknown. Based on a Lyapunov-like analysis, we show that all adjustable parameters and internal signals remain bounded for all iterations. Furthermore, the norm of state tracking error vector will asymptotically converge to a tunable residual set as iteration goes to infinity. Finally, iterative learning control of two nonlinear systems, inverted pendulum system and Chua's chaotic circuit, are performed to verify the tracking performance of the proposed learning scheme.

  7. Adaptive Boundary Iterative Learning Control for an Euler-Bernoulli Beam System With Input Constraint.

    PubMed

    He, Wei; Meng, Tingting; Huang, Deqing; Li, Xuefang

    2017-03-15

    This paper addresses the vibration control and the input constraint for an Euler-Bernoulli beam system under aperiodic distributed disturbance and aperiodic boundary disturbance. Hyperbolic tangent functions and saturation functions are adopted to tackle the input constraint. A restrained adaptive boundary iterative learning control (ABILC) law is proposed based on a time-weighted Lyapunov-Krasovskii-like composite energy function. In order to deal with the uncertainty of a system parameter and reject the external disturbances, three adaptive laws are designed and learned in the iteration domain. All the system states of the closed-loop system are proved to be bounded in each iteration. Along the iteration axis, the displacements asymptotically converge toward zero. Simulation results are provided to illustrate the effectiveness of the proposed ABILC scheme.

  8. Iterative learning control and the singularity robust Jacobian inverse for mobile manipulators

    NASA Astrophysics Data System (ADS)

    Tchoń, Krzysztof

    2010-11-01

    The method of iterative learning control, to a large extent, has been inspired by robotics research, focused on the control of stationary manipulators. In this article we deal with the inverse kinematics problem for mobile manipulators, and show that a very basic singularity robust Jacobian inverse can be derived in a natural way within the framework of iterative learning control. To achieve this objective we have exploited the endogenous configuration space approach. The introduced Jacobian inverse defines the singularity robust Jacobian inverse kinematics algorithm for mobile manipulators. A Kantorovich-type estimate of the region of guaranteed convergence of the algorithm is derived. For two example kinematics, this estimate has been computed efficiently.

  9. Adaptive iterative learning control for a class of non-linearly parameterised systems with input saturations

    NASA Astrophysics Data System (ADS)

    Zhang, Ruikun; Hou, Zhongsheng; Ji, Honghai; Yin, Chenkun

    2016-04-01

    In this paper, an adaptive iterative learning control scheme is proposed for a class of non-linearly parameterised systems with unknown time-varying parameters and input saturations. By incorporating a saturation function, a new iterative learning control mechanism is presented which includes a feedback term and a parameter updating term. Through the use of parameter separation technique, the non-linear parameters are separated from the non-linear function and then a saturated difference updating law is designed in iteration domain by combining the unknown parametric term of the local Lipschitz continuous function and the unknown time-varying gain into an unknown time-varying function. The analysis of convergence is based on a time-weighted Lyapunov-Krasovskii-like composite energy function which consists of time-weighted input, state and parameter estimation information. The proposed learning control mechanism warrants a L2[0, T] convergence of the tracking error sequence along the iteration axis. Simulation results are provided to illustrate the effectiveness of the adaptive iterative learning control scheme.

  10. Implementation of a new iterative learning control algorithm on real data

    NASA Astrophysics Data System (ADS)

    Zamanian, Hamed; Koohi, Ardavan

    2016-02-01

    In this paper, a newly presented approach is proposed for closed-loop automatic tuning of a proportional integral derivative (PID) controller based on iterative learning control (ILC) algorithm. A modified ILC scheme iteratively changes the control signal by adjusting it. Once a satisfactory performance is achieved, a linear compensator is identified in the ILC behavior using casual relationship between the closed loop signals. This compensator is approximated by a PD controller which is used to tune the original PID controller. Results of implementing this approach presented on the experimental data of Damavand tokamak and are consistent with simulation outcome.

  11. Iterative learning control with unknown control direction: a novel data-based approach.

    PubMed

    Shen, Dong; Hou, Zhongsheng

    2011-12-01

    Iterative learning control (ILC) is considered for both deterministic and stochastic systems with unknown control direction. To deal with the unknown control direction, a novel switching mechanism, based only on available system tracking error data, is first proposed. Then two ILC algorithms combined with the novel switching mechanism are designed for both deterministic and stochastic systems. It is proved that the ILC algorithms would switch to the right control direction and stick to it after a finite number of cycles. Moreover, the input sequence converges to the desired one under the deterministic case. The input sequence converges to the optimal one with probability 1 under stochastic case and the resulting tracking error tends to its minimal value.

  12. Control of a pneumatic power active lower-limb orthosis with filter-based iterative learning control

    NASA Astrophysics Data System (ADS)

    Huang, Chia-En; Chen, Jian-Shiang

    2014-05-01

    A filter-based iterative learning control (FILC) scheme is developed in this paper, which consists in a proportional-derivative (PD) feedback controller and a feedforward filter. Moreover, based on two-dimensional system theory, the stability of the FILC system is proven. The design criteria for a wavelet transform filter (WTF) - chosen as the feedforward filter - and the PD feedback controller are also given. Finally, using a pneumatic power active lower-limb orthosis (PPALO) as the controlled plant, the wavelet-based iterative learning control (WILC) implementation and the orchestration of a trajectory tracking control simulation are given in detail and the overall tracking performance is validated.

  13. An iterative learning control method with application for CNC machine tools

    SciTech Connect

    Kim, D.I.; Kim, S.

    1996-01-01

    A proportional, integral, and derivative (PID) type iterative learning controller is proposed for precise tracking control of industrial robots and computer numerical controller (CNC) machine tools performing repetitive tasks. The convergence of the output error by the proposed learning controller is guaranteed under a certain condition even when the system parameters are not known exactly and unknown external disturbances exist. As the proposed learning controller is repeatedly applied to the industrial robot or the CNC machine tool with the path-dependent repetitive task, the distance difference between the desired path and the actual tracked or machined path, which is one of the most significant factors in the evaluation of control performance, is progressively reduced. The experimental results demonstrate that the proposed learning controller can improve machining accuracy when the CNC machine tool performs repetitive machining tasks.

  14. Distributed adaptive fuzzy iterative learning control of coordination problems for higher order multi-agent systems

    NASA Astrophysics Data System (ADS)

    Li, Jinsha; Li, Junmin

    2016-07-01

    In this paper, the adaptive fuzzy iterative learning control scheme is proposed for coordination problems of Mth order (M ≥ 2) distributed multi-agent systems. Every follower agent has a higher order integrator with unknown nonlinear dynamics and input disturbance. The dynamics of the leader are a higher order nonlinear systems and only available to a portion of the follower agents. With distributed initial state learning, the unified distributed protocols combined time-domain and iteration-domain adaptive laws guarantee that the follower agents track the leader uniformly on [0, T]. Then, the proposed algorithm extends to achieve the formation control. A numerical example and a multiple robotic system are provided to demonstrate the performance of the proposed approach.

  15. Application of a repetitive process setting to design of monotonically convergent iterative learning control

    NASA Astrophysics Data System (ADS)

    Boski, Marcin; Paszke, Wojciech

    2015-11-01

    This paper deals with the problem of designing an iterative learning control algorithm for discrete linear systems using repetitive process stability theory. The resulting design produces a stabilizing output feedback controller in the time domain and a feedforward controller that guarantees monotonic convergence in the trial-to-trial domain. The results are also extended to limited frequency range design specification. New design procedure is introduced in terms of linear matrix inequality (LMI) representations, which guarantee the prescribed performances of ILC scheme. A simulation example is given to illustrate the theoretical developments.

  16. Task-Space Iterative Learning for Redundant Robotic Systems: Existence of a Task-Space Control and Convergence of Learning

    NASA Astrophysics Data System (ADS)

    Arimoto, Suguru; Sekimoto, Masahiro; Kawamura, Sadao

    This paper presents a feasibility study of iterative learning control for a class of redundant multi-joint robotic systems when a desired motion trajectory is specified in task-space with less dimension than that of joint space. First, it is shown that if the desired trajectory described in task-space for a time interval t ∈ [0,T] is twice continuously differentiable then a unique control signal describable in task-space exists despite of the system joint-redundancy. Second, a learning control update law is constructed through transpose of the Jacobian matrix of task-space coordinates with respect to joint coordinates by using measured data of motion trajectories in task-space. Third, the convergence of trajectory trackings through iterative learning is proved theoretically on the basis of original nonlinear robot dynamics in joint space.

  17. Iterative learning control with applications in energy generation, lasers and health care

    PubMed Central

    Tutty, O. R.

    2016-01-01

    Many physical systems make repeated executions of the same finite time duration task. One example is a robot in a factory or warehouse whose task is to collect an object in sequence from a location, transfer it over a finite duration, place it at a specified location or on a moving conveyor and then return for the next one and so on. Iterative learning control was especially developed for systems with this mode of operation and this paper gives an overview of this control design method using relatively recent relevant applications in wind turbines, free-electron lasers and health care, as exemplars to demonstrate its applicability. PMID:27713654

  18. Iterative learning control with applications in energy generation, lasers and health care

    NASA Astrophysics Data System (ADS)

    Rogers, E.; Tutty, O. R.

    2016-09-01

    Many physical systems make repeated executions of the same finite time duration task. One example is a robot in a factory or warehouse whose task is to collect an object in sequence from a location, transfer it over a finite duration, place it at a specified location or on a moving conveyor and then return for the next one and so on. Iterative learning control was especially developed for systems with this mode of operation and this paper gives an overview of this control design method using relatively recent relevant applications in wind turbines, free-electron lasers and health care, as exemplars to demonstrate its applicability.

  19. Networked iterative learning control approach for nonlinear systems with random communication delay

    NASA Astrophysics Data System (ADS)

    Liu, Jian; Ruan, Xiaoe

    2016-12-01

    This paper constructs a proportional-type networked iterative learning control (NILC) scheme for a class of discrete-time nonlinear systems with the stochastic data communication delay within one operation duration and being subject to Bernoulli-type distribution. In the scheme, the communication delayed data is replaced by successfully captured one at the concurrent sampling moment of the latest iteration. The tracking performance of the addressed NILC algorithm is analysed by statistic technique in virtue of mathematical expectation. The analysis shows that, under certain conditions, the expectation of the tracking error measured in the form of 1-norm is asymptotically convergent to zero. Numerical experiments are carried out to illustrate the validity and effectiveness.

  20. Model-based iterative learning control of Parkinsonian state in thalamic relay neuron

    NASA Astrophysics Data System (ADS)

    Liu, Chen; Wang, Jiang; Li, Huiyan; Xue, Zhiqin; Deng, Bin; Wei, Xile

    2014-09-01

    Although the beneficial effects of chronic deep brain stimulation on Parkinson's disease motor symptoms are now largely confirmed, the underlying mechanisms behind deep brain stimulation remain unclear and under debate. Hence, the selection of stimulation parameters is full of challenges. Additionally, due to the complexity of neural system, together with omnipresent noises, the accurate model of thalamic relay neuron is unknown. Thus, the iterative learning control of the thalamic relay neuron's Parkinsonian state based on various variables is presented. Combining the iterative learning control with typical proportional-integral control algorithm, a novel and efficient control strategy is proposed, which does not require any particular knowledge on the detailed physiological characteristics of cortico-basal ganglia-thalamocortical loop and can automatically adjust the stimulation parameters. Simulation results demonstrate the feasibility of the proposed control strategy to restore the fidelity of thalamic relay in the Parkinsonian condition. Furthermore, through changing the important parameter—the maximum ionic conductance densities of low-threshold calcium current, the dominant characteristic of the proposed method which is independent of the accurate model can be further verified.

  1. Realization of Comfortable Massage by Using Iterative Learning Control Based on EEG

    NASA Astrophysics Data System (ADS)

    Teramae, Tatsuya; Kushida, Daisuke; Takemori, Fumiaki; Kitamura, Akira

    Recently the massage chair is used by a lot of people because they are able to use it easily at home. However a present massage chair only realizes the massage motion. Moreover the massage chair can not consider the user’s condition and massage force. On the other hand, the professional masseur is according to presume the mental condition by patient’s reaction. Then this paper proposes the method of applying masseur’s procedure for the massage chair using iterative learning control based on EEG. And massage force is estimated by acceleration sensor. The realizability of the proposed method is verified by the experimental works using the massage chair.

  2. Multivariable norm optimal and parameter optimal iterative learning control: a unified formulation

    NASA Astrophysics Data System (ADS)

    Owens, D. H.

    2012-08-01

    This article investigates the two paradigms of norm optimal iterative learning control (NOILC) and parameter optimal iterative learning control (POILC) for multivariable (MIMO) ℓ-input, m-output linear discrete-time systems. The main result is a proof that, despite their algebraic and conceptual differences, they can be unified using linear quadratic multi-parameter optimisation techniques. In particular, whilst POILC has been naturally regarded as an approximation to NOILC, it is shown that the NOILC control law can be generated from a suitable choice of control law parameterisation and objective function in a multi-parameter MIMO POILC problem. The form of this equivalence is used to propose a new general approach to the construction of POILC problems for MIMO systems that approximates the solution of a given NOILC problem. An infinite number of such approximations exist. This great diversity is illustrated by the derivation of new convergent algorithms based on time interval and gradient partition that extend previously published work.

  3. Iterative Learning Control Systems Based on Inverse Systems and Interactor Matrix for Linear Discrete-time Systems

    NASA Astrophysics Data System (ADS)

    Kase, Wataru

    In this paper, it will be clear the structure of the Iterative Learning Control (ILC) based on the inverse system. Moore-Penrose pseudo-inverse of a Toeplitz matrix will be investigated to analyze the learning gain matrix and will be derived the cascade controller transfer function matrix. From these investigations, the critical points of ILC based on the gradient will be issued.

  4. Effects of iterative learning based signal control strategies on macroscopic fundamental diagrams of urban road networks

    NASA Astrophysics Data System (ADS)

    Yan, Fei; Tian, Fuli; Shi, Zhongke

    2016-10-01

    Urban traffic flows are inherently repeated on a daily or weekly basis. This repeatability can help improve the traffic conditions if it is used properly by the control system. In this paper, we propose a novel iterative learning control (ILC) strategy for traffic signals of urban road networks using the repeatability feature of traffic flow. To improve the control robustness, the ILC strategy is further integrated with an error feedback control law in a complementary manner. Theoretical analysis indicates that the ILC-based traffic signal control methods can guarantee the asymptotic learning convergence, despite the presence of modeling uncertainties and exogenous disturbances. Finally, the impacts of the ILC-based signal control strategies on the network macroscopic fundamental diagram (MFD) are examined. The results show that the proposed ILC-based control strategies can homogenously distribute the network accumulation by controlling the vehicle numbers in each link to the desired levels under different traffic demands, which can result in the network with high capacity and mobility.

  5. A 2D systems approach to iterative learning control for discrete linear processes with zero Markov parameters

    NASA Astrophysics Data System (ADS)

    Hladowski, Lukasz; Galkowski, Krzysztof; Cai, Zhonglun; Rogers, Eric; Freeman, Chris T.; Lewin, Paul L.

    2011-07-01

    In this article a new approach to iterative learning control for the practically relevant case of deterministic discrete linear plants with uniform rank greater than unity is developed. The analysis is undertaken in a 2D systems setting that, by using a strong form of stability for linear repetitive processes, allows simultaneous consideration of both trial-to-trial error convergence and along the trial performance, resulting in design algorithms that can be computed using linear matrix inequalities (LMIs). Finally, the control laws are experimentally verified on a gantry robot that replicates a pick and place operation commonly found in a number of applications to which iterative learning control is applicable.

  6. Indirect iterative learning control for a discrete visual servo without a camera-robot model.

    PubMed

    Jiang, Ping; Bamforth, Leon C A; Feng, Zuren; Baruch, John E F; Chen, YangQuan

    2007-08-01

    This paper presents a discrete learning controller for vision-guided robot trajectory imitation with no prior knowledge of the camera-robot model. A teacher demonstrates a desired movement in front of a camera, and then, the robot is tasked to replay it by repetitive tracking. The imitation procedure is considered as a discrete tracking control problem in the image plane, with an unknown and time-varying image Jacobian matrix. Instead of updating the control signal directly, as is usually done in iterative learning control (ILC), a series of neural networks are used to approximate the unknown Jacobian matrix around every sample point in the demonstrated trajectory, and the time-varying weights of local neural networks are identified through repetitive tracking, i.e., indirect ILC. This makes repetitive segmented training possible, and a segmented training strategy is presented to retain the training trajectories solely within the effective region for neural network approximation. However, a singularity problem may occur if an unmodified neural-network-based Jacobian estimation is used to calculate the robot end-effector velocity. A new weight modification algorithm is proposed which ensures invertibility of the estimation, thus circumventing the problem. Stability is further discussed, and the relationship between the approximation capability of the neural network and the tracking accuracy is obtained. Simulations and experiments are carried out to illustrate the validity of the proposed controller for trajectory imitation of robot manipulators with unknown time-varying Jacobian matrices.

  7. Height control of laser metal-wire deposition based on iterative learning control and 3D scanning

    NASA Astrophysics Data System (ADS)

    Heralić, Almir; Christiansson, Anna-Karin; Lennartson, Bengt

    2012-09-01

    Laser Metal-wire Deposition is an additive manufacturing technique for solid freeform fabrication of fully dense metal structures. The technique is based on robotized laser welding and wire filler material, and the structures are built up layer by layer. The deposition process is, however, sensitive to disturbances and thus requires continuous monitoring and adjustments. In this work a 3D scanning system is developed and integrated with the robot control system for automatic in-process control of the deposition. The goal is to ensure stable deposition, by means of choosing a correct offset of the robot in the vertical direction, and obtaining a flat surface, for each deposited layer. The deviations in the layer height are compensated by controlling the wire feed rate on next deposition layer, based on the 3D scanned data, by means of iterative learning control. The system is tested through deposition of bosses, which is expected to be a typical application for this technique in the manufacture of jet engine components. The results show that iterative learning control including 3D scanning is a suitable method for automatic deposition of such structures. This paper presents the equipment, the control strategy and demonstrates the proposed approach with practical experiments.

  8. Design of robust iterative learning control schemes for systems with polytopic uncertainties and sector-bounded nonlinearities

    NASA Astrophysics Data System (ADS)

    Boski, Marcin; Paszke, Wojciech

    2017-01-01

    This paper deals with designing of iterative learning control schemes for uncertain systems with static nonlinearities. More specifically, the nonlinear part is supposed to be sector bounded and system matrices are assumed to range in the polytope of matrices. For systems with such nonlinearities and uncertainties the repetitive process setting is exploited to develop a linear matrix inequality based conditions for computing the feedback and feedforward (learning) controllers. These controllers guarantee acceptable dynamics along the trials and ensure convergence of the trial-to-trial error dynamics, respectively. Numerical examples illustrate the theoretical results and confirm effectiveness of the designed control scheme.

  9. An inverse-model approach to multivariable norm optimal iterative learning control with auxiliary optimisation

    NASA Astrophysics Data System (ADS)

    Owens, David H.; Freeman, Chris T.; Chu, Bing

    2014-08-01

    Motivated by the commonly encountered problem in which tracking is only required at selected intermediate points within the time interval, a general optimisation-based iterative learning control (ILC) algorithm is derived that ensures convergence of tracking errors to zero whilst simultaneously minimising a specified quadratic objective function of the input signals and chosen auxiliary (state) variables. In practice, the proposed solutions enable a repeated tracking task to be accurately completed whilst simultaneously reducing undesirable effects such as payload spillage, vibration tendencies and actuator wear. The theory is developed using the well-known norm optimal ILC (NOILC) framework, using general linear, functional operators between real Hilbert spaces. Solutions are derived using feedforward action, convergence is proved and robustness bounds are presented using both norm bounds and positivity conditions. Algorithms are specified for both continuous and discrete-time state-space representations, with the latter including application to multi-rate sampled systems. Experimental results using a robotic manipulator confirm the practical utility of the algorithms and the closeness with which observed results match theoretical predictions.

  10. Model-Free Primitive-Based Iterative Learning Control Approach to Trajectory Tracking of MIMO Systems With Experimental Validation.

    PubMed

    Radac, Mircea-Bogdan; Precup, Radu-Emil; Petriu, Emil M

    2015-11-01

    This paper proposes a novel model-free trajectory tracking of multiple-input multiple-output (MIMO) systems by the combination of iterative learning control (ILC) and primitives. The optimal trajectory tracking solution is obtained in terms of previously learned solutions to simple tasks called primitives. The library of primitives that are stored in memory consists of pairs of reference input/controlled output signals. The reference input primitives are optimized in a model-free ILC framework without using knowledge of the controlled process. The guaranteed convergence of the learning scheme is built upon a model-free virtual reference feedback tuning design of the feedback decoupling controller. Each new complex trajectory to be tracked is decomposed into the output primitives regarded as basis functions. The optimal reference input for the control system to track the desired trajectory is next recomposed from the reference input primitives. This is advantageous because the optimal reference input is computed straightforward without the need to learn from repeated executions of the tracking task. In addition, the optimization problem specific to trajectory tracking of square MIMO systems is decomposed in a set of optimization problems assigned to each separate single-input single-output control channel that ensures a convenient model-free decoupling. The new model-free primitive-based ILC approach is capable of planning, reasoning, and learning. A case study dealing with the model-free control tuning for a nonlinear aerodynamic system is included to validate the new approach. The experimental results are given.

  11. Functional electrical stimulation mediated by iterative learning control and 3D robotics reduces motor impairment in chronic stroke.

    PubMed

    Meadmore, Katie L; Hughes, Ann-Marie; Freeman, Chris T; Cai, Zhonglun; Tong, Daisy; Burridge, Jane H; Rogers, Eric

    2012-06-07

    Novel stroke rehabilitation techniques that employ electrical stimulation (ES) and robotic technologies are effective in reducing upper limb impairments. ES is most effective when it is applied to support the patients' voluntary effort; however, current systems fail to fully exploit this connection. This study builds on previous work using advanced ES controllers, and aims to investigate the feasibility of Stimulation Assistance through Iterative Learning (SAIL), a novel upper limb stroke rehabilitation system which utilises robotic support, ES, and voluntary effort. Five hemiparetic, chronic stroke participants with impaired upper limb function attended 18, 1 hour intervention sessions. Participants completed virtual reality tracking tasks whereby they moved their impaired arm to follow a slowly moving sphere along a specified trajectory. To do this, the participants' arm was supported by a robot. ES, mediated by advanced iterative learning control (ILC) algorithms, was applied to the triceps and anterior deltoid muscles. Each movement was repeated 6 times and ILC adjusted the amount of stimulation applied on each trial to improve accuracy and maximise voluntary effort. Participants completed clinical assessments (Fugl-Meyer, Action Research Arm Test) at baseline and post-intervention, as well as unassisted tracking tasks at the beginning and end of each intervention session. Data were analysed using t-tests and linear regression. From baseline to post-intervention, Fugl-Meyer scores improved, assisted and unassisted tracking performance improved, and the amount of ES required to assist tracking reduced. The concept of minimising support from ES using ILC algorithms was demonstrated. The positive results are promising with respect to reducing upper limb impairments following stroke, however, a larger study is required to confirm this.

  12. Using Functional Electrical Stimulation Mediated by Iterative Learning Control and Robotics to Improve Arm Movement for People With Multiple Sclerosis.

    PubMed

    Sampson, Patrica; Freeman, Chris; Coote, Susan; Demain, Sara; Feys, Peter; Meadmore, Katie; Hughes, Ann-Marie

    2016-02-01

    Few interventions address multiple sclerosis (MS) arm dysfunction but robotics and functional electrical stimulation (FES) appear promising. This paper investigates the feasibility of combining FES with passive robotic support during virtual reality (VR) training tasks to improve upper limb function in people with multiple sclerosis (pwMS). The system assists patients in following a specified trajectory path, employing an advanced model-based paradigm termed iterative learning control (ILC) to adjust the FES to improve accuracy and maximise voluntary effort. Reaching tasks were repeated six times with ILC learning the optimum control action from previous attempts. A convenience sample of five pwMS was recruited from local MS societies, and the intervention comprised 18 one-hour training sessions over 10 weeks. The accuracy of tracking performance without FES and the amount of FES delivered during training were analyzed using regression analysis. Clinical functioning of the arm was documented before and after treatment with standard tests. Statistically significant results following training included: improved accuracy of tracking performance both when assisted and unassisted by FES; reduction in maximum amount of FES needed to assist tracking; and less impairment in the proximal arm that was trained. The system was well tolerated by all participants with no increase in muscle fatigue reported. This study confirms the feasibility of FES combined with passive robot assistance as a potentially effective intervention to improve arm movement and control in pwMS and provides the basis for a follow-up study.

  13. ITER Plasma Control System Development

    NASA Astrophysics Data System (ADS)

    Snipes, Joseph; ITER PCS Design Team

    2015-11-01

    The development of the ITER Plasma Control System (PCS) continues with the preliminary design phase for 1st plasma and early plasma operation in H/He up to Ip = 15 MA in L-mode. The design is being developed through a contract between the ITER Organization and a consortium of plasma control experts from EU and US fusion laboratories, which is expected to be completed in time for a design review at the end of 2016. This design phase concentrates on breakdown including early ECH power and magnetic control of the poloidal field null, plasma current, shape, and position. Basic kinetic control of the heating (ECH, ICH, NBI) and fueling systems is also included. Disruption prediction, mitigation, and maintaining stable operation are also included because of the high magnetic and kinetic stored energy present already for early plasma operation. Support functions for error field topology and equilibrium reconstruction are also required. All of the control functions also must be integrated into an architecture that will be capable of the required complexity of all ITER scenarios. A database is also being developed to collect and manage PCS functional requirements from operational scenarios that were defined in the Conceptual Design with links to proposed event handling strategies and control algorithms for initial basic control functions. A brief status of the PCS development will be presented together with a proposed schedule for design phases up to DT operation.

  14. Iterative learning control for synchronization of reticle stage and wafer stage in step-and-scan lithographic equipment

    NASA Astrophysics Data System (ADS)

    Li, Lan-lan; Hu, Song; Zhao, Li-xin; Ma, Ping

    2013-08-01

    Lithographic equipments are highly complex machines used to manufacture integrated circuits (ICs). To make larger ICs, a larger lens is required, which, however, is prohibitively expensive. The solution to this problem is to expose a chip not in one flash but in a scanning fashion. For step-and-scan lithographic equipment (wafer scanner), the image quality is decided by many factors, in which synchronization of reticle stage and wafer stage during exposure is a key one. In this paper, the principle of reticle stage and wafer stage was analyzed through investigating the structure of scanners, firstly. While scanning, the reticle stage and wafer stage should scan simultaneously at a high speed and the speed ratio is 1:4. Secondly, an iterative learning controller (ILC) for synchronization of reticle stage and wafer stage is presented. In the controller, a master-slave structure is used, with the wafer stage acting as the master, and the reticle stage as the slave. Since the scanning process of scanner is repetitive, ILC is used to improve tracking performance. A simple design procedure is presented which allows design of the ILC system for the reticle stage and wafer stage independently. Finally, performance of the algorithm is illustrated by simulated on the virtual stages (the reticle stage and wafer stage).The results of simulation experiments and theory analyzing demonstrate that using the proposed controller better synchronization performance can be obtained for the reticle stage and wafer stage in scanner. Theory analysis and experiment shows the method is reasonable and efficient.

  15. Iterated learning and the evolution of language.

    PubMed

    Kirby, Simon; Griffiths, Tom; Smith, Kenny

    2014-10-01

    Iterated learning describes the process whereby an individual learns their behaviour by exposure to another individual's behaviour, who themselves learnt it in the same way. It can be seen as a key mechanism of cultural evolution. We review various methods for understanding how behaviour is shaped by the iterated learning process: computational agent-based simulations; mathematical modelling; and laboratory experiments in humans and non-human animals. We show how this framework has been used to explain the origins of structure in language, and argue that cultural evolution must be considered alongside biological evolution in explanations of language origins. Copyright © 2014 Elsevier Ltd. All rights reserved.

  16. Eliminating unpredictable variation through iterated learning.

    PubMed

    Smith, Kenny; Wonnacott, Elizabeth

    2010-09-01

    Human languages may be shaped not only by the (individual psychological) processes of language acquisition, but also by population-level processes arising from repeated language learning and use. One prevalent feature of natural languages is that they avoid unpredictable variation. The current work explores whether linguistic predictability might result from a process of iterated learning in simple diffusion chains of adults. An iterated artificial language learning methodology was used, in which participants were organised into diffusion chains: the first individual in each chain was exposed to an artificial language which exhibited unpredictability in plural marking, and subsequent learners were exposed to the language produced by the previous learner in their chain. Diffusion chains, but not isolate learners, were found to cumulatively increase predictability of plural marking by lexicalising the choice of plural marker. This suggests that such gradual, cumulative population-level processes offer a possible explanation for regularity in language. 2010 Elsevier B.V. All rights reserved.

  17. Language Evolution by Iterated Learning with Bayesian Agents

    ERIC Educational Resources Information Center

    Griffiths, Thomas L.; Kalish, Michael L.

    2007-01-01

    Languages are transmitted from person to person and generation to generation via a process of iterated learning: people learn a language from other people who once learned that language themselves. We analyze the consequences of iterated learning for learning algorithms based on the principles of Bayesian inference, assuming that learners compute…

  18. Language Evolution by Iterated Learning with Bayesian Agents

    ERIC Educational Resources Information Center

    Griffiths, Thomas L.; Kalish, Michael L.

    2007-01-01

    Languages are transmitted from person to person and generation to generation via a process of iterated learning: people learn a language from other people who once learned that language themselves. We analyze the consequences of iterated learning for learning algorithms based on the principles of Bayesian inference, assuming that learners compute…

  19. Novel aspects of plasma control in ITER

    NASA Astrophysics Data System (ADS)

    Humphreys, D.; Ambrosino, G.; de Vries, P.; Felici, F.; Kim, S. H.; Jackson, G.; Kallenbach, A.; Kolemen, E.; Lister, J.; Moreau, D.; Pironti, A.; Raupp, G.; Sauter, O.; Schuster, E.; Snipes, J.; Treutterer, W.; Walker, M.; Welander, A.; Winter, A.; Zabeo, L.

    2015-02-01

    ITER plasma control design solutions and performance requirements are strongly driven by its nuclear mission, aggressive commissioning constraints, and limited number of operational discharges. In addition, high plasma energy content, heat fluxes, neutron fluxes, and very long pulse operation place novel demands on control performance in many areas ranging from plasma boundary and divertor regulation to plasma kinetics and stability control. Both commissioning and experimental operations schedules provide limited time for tuning of control algorithms relative to operating devices. Although many aspects of the control solutions required by ITER have been well-demonstrated in present devices and even designed satisfactorily for ITER application, many elements unique to ITER including various crucial integration issues are presently under development. We describe selected novel aspects of plasma control in ITER, identifying unique parts of the control problem and highlighting some key areas of research remaining. Novel control areas described include control physics understanding (e.g., current profile regulation, tearing mode (TM) suppression), control mathematics (e.g., algorithmic and simulation approaches to high confidence robust performance), and integration solutions (e.g., methods for management of highly subscribed control resources). We identify unique aspects of the ITER TM suppression scheme, which will pulse gyrotrons to drive current within a magnetic island, and turn the drive off following suppression in order to minimize use of auxiliary power and maximize fusion gain. The potential role of active current profile control and approaches to design in ITER are discussed. Issues and approaches to fault handling algorithms are described, along with novel aspects of actuator sharing in ITER.

  20. Novel aspects of plasma control in ITER

    DOE PAGES

    Humphreys, David; Ambrosino, G.; de Vries, Peter; ...

    2015-02-12

    ITER plasma control design solutions and performance requirements are strongly driven by its nuclear mission, aggressive commissioning constraints, and limited number of operational discharges. In addition, high plasma energy content, heat fluxes, neutron fluxes, and very long pulse operation place novel demands on control performance in many areas ranging from plasma boundary and divertor regulation to plasma kinetics and stability control. Both commissioning and experimental operations schedules provide limited time for tuning of control algorithms relative to operating devices. Although many aspects of the control solutions required by ITER have been well-demonstrated in present devices and even designed satisfactorily formore » ITER application, many elements unique to ITER including various crucial integration issues are presently under development. We describe selected novel aspects of plasma control in ITER, identifying unique parts of the control problem and highlighting some key areas of research remaining. Novel control areas described include control physics understanding (e.g. current profile regulation, tearing mode suppression (TM)), control mathematics (e.g. algorithmic and simulation approaches to high confidence robust performance), and integration solutions (e.g. methods for management of highly-subscribed control resources). We identify unique aspects of the ITER TM suppression scheme, which will pulse gyrotrons to drive current within a magnetic island, and turn the drive off following suppression in order to minimize use of auxiliary power and maximize fusion gain. The potential role of active current profile control and approaches to design in ITER are discussed. Finally, issues and approaches to fault handling algorithms are described, along with novel aspects of actuator sharing in ITER.« less

  1. Novel aspects of plasma control in ITER

    SciTech Connect

    Humphreys, David; Ambrosino, G.; Felici, Federico; Kim, Sun H.; Jackson, Gary; Kallenbach, A.; Kolemen, Egemen; Lister, J.; Moreau, D.; Pironti, A.; Sauter, O.; Schuster, E.; Snipes, J.; Treutterer, W.; Walker, M.; Welander, A.; Winter, A.; Zabeo, L.

    2015-02-12

    ITER plasma control design solutions and performance requirements are strongly driven by its nuclear mission, aggressive commissioning constraints, and limited number of operational discharges. In addition, high plasma energy content, heat fluxes, neutron fluxes, and very long pulse operation place novel demands on control performance in many areas ranging from plasma boundary and divertor regulation to plasma kinetics and stability control. Both commissioning and experimental operations schedules provide limited time for tuning of control algorithms relative to operating devices. Although many aspects of the control solutions required by ITER have been well-demonstrated in present devices and even designed satisfactorily for ITER application, many elements unique to ITER including various crucial integration issues are presently under development. We describe selected novel aspects of plasma control in ITER, identifying unique parts of the control problem and highlighting some key areas of research remaining. Novel control areas described include control physics understanding (e.g. current profile regulation, tearing mode suppression (TM)), control mathematics (e.g. algorithmic and simulation approaches to high confidence robust performance), and integration solutions (e.g. methods for management of highly-subscribed control resources). We identify unique aspects of the ITER TM suppression scheme, which will pulse gyrotrons to drive current within a magnetic island, and turn the drive off following suppression in order to minimize use of auxiliary power and maximize fusion gain. The potential role of active current profile control and approaches to design in ITER are discussed. Finally, issues and approaches to fault handling algorithms are described, along with novel aspects of actuator sharing in ITER.

  2. Learning to improve iterative repair scheduling

    NASA Technical Reports Server (NTRS)

    Zweben, Monte; Davis, Eugene

    1992-01-01

    This paper presents a general learning method for dynamically selecting between repair heuristics in an iterative repair scheduling system. The system employs a version of explanation-based learning called Plausible Explanation-Based Learning (PEBL) that uses multiple examples to confirm conjectured explanations. The basic approach is to conjecture contradictions between a heuristic and statistics that measure the quality of the heuristic. When these contradictions are confirmed, a different heuristic is selected. To motivate the utility of this approach we present an empirical evaluation of the performance of a scheduling system with respect to two different repair strategies. We show that the scheduler that learns to choose between the heuristics outperforms the same scheduler with any one of two heuristics alone.

  3. ITER Shape Controller and Transport Simulations

    SciTech Connect

    Casper, T A; Meyer, W H; Pearlstein, L D; Portone, A

    2007-05-31

    We currently use the CORSICA integrated modeling code for scenario studies for both the DIII-D and ITER experiments. In these simulations, free- or fixed-boundary equilibria are simultaneously converged with thermal evolution determined from transport models providing temperature and current density profiles. Using a combination of fixed boundary evolution followed by free-boundary calculation to determine the separatrix and coil currents. In the free-boundary calculation, we use the state-space controller representation with transport simulations to provide feedback modeling of shape, vertical stability and profile control. In addition to a tightly coupled calculation with simulator and controller imbedded inside CORSICA, we also use a remote procedure call interface to couple the CORSICA non-linear plasma simulations to the controller environments developed within the Mathworks Matlab/Simulink environment. We present transport simulations using full shape and vertical stability control with evolution of the temperature profiles to provide simulations of the ITER controller and plasma response.

  4. Quantized Iterative Learning Consensus Tracking of Digital Networks With Limited Information Communication.

    PubMed

    Xiong, Wenjun; Yu, Xinghuo; Chen, Yao; Gao, Jie

    2016-03-03

    This brief investigates the quantized iterative learning problem for digital networks with time-varying topologies. The information is first encoded as symbolic data and then transmitted. After the data are received, a decoder is used by the receiver to get an estimate of the sender's state. Iterative learning quantized communication is considered in the process of encoding and decoding. A sufficient condition is then presented to achieve the consensus tracking problem in a finite interval using the quantized iterative learning controllers. Finally, simulation results are given to illustrate the usefulness of the developed criterion.

  5. Iter

    NASA Astrophysics Data System (ADS)

    Iotti, Robert

    2015-04-01

    ITER is an international experimental facility being built by seven Parties to demonstrate the long term potential of fusion energy. The ITER Joint Implementation Agreement (JIA) defines the structure and governance model of such cooperation. There are a number of necessary conditions for such international projects to be successful: a complete design, strong systems engineering working with an agreed set of requirements, an experienced organization with systems and plans in place to manage the project, a cost estimate backed by industry, and someone in charge. Unfortunately for ITER many of these conditions were not present. The paper discusses the priorities in the JIA which led to setting up the project with a Central Integrating Organization (IO) in Cadarache, France as the ITER HQ, and seven Domestic Agencies (DAs) located in the countries of the Parties, responsible for delivering 90%+ of the project hardware as Contributions-in-Kind and also financial contributions to the IO, as ``Contributions-in-Cash.'' Theoretically the Director General (DG) is responsible for everything. In practice the DG does not have the power to control the work of the DAs, and there is not an effective management structure enabling the IO and the DAs to arbitrate disputes, so the project is not really managed, but is a loose collaboration of competing interests. Any DA can effectively block a decision reached by the DG. Inefficiencies in completing design while setting up a competent organization from scratch contributed to the delays and cost increases during the initial few years. So did the fact that the original estimate was not developed from industry input. Unforeseen inflation and market demand on certain commodities/materials further exacerbated the cost increases. Since then, improvements are debatable. Does this mean that the governance model of ITER is a wrong model for international scientific cooperation? I do not believe so. Had the necessary conditions for success

  6. Eliminating Unpredictable Variation through Iterated Learning

    ERIC Educational Resources Information Center

    Smith, Kenny; Wonnacott, Elizabeth

    2010-01-01

    Human languages may be shaped not only by the (individual psychological) processes of language acquisition, but also by population-level processes arising from repeated language learning and use. One prevalent feature of natural languages is that they avoid unpredictable variation. The current work explores whether linguistic predictability might…

  7. Iterative learning-based decentralized adaptive tracker for large-scale systems: a digital redesign approach.

    PubMed

    Tsai, Jason Sheng-Hong; Du, Yan-Yi; Huang, Pei-Hsiang; Guo, Shu-Mei; Shieh, Leang-San; Chen, Yuhua

    2011-07-01

    In this paper, a digital redesign methodology of the iterative learning-based decentralized adaptive tracker is proposed to improve the dynamic performance of sampled-data linear large-scale control systems consisting of N interconnected multi-input multi-output subsystems, so that the system output will follow any trajectory which may not be presented by the analytic reference model initially. To overcome the interference of each sub-system and simplify the controller design, the proposed model reference decentralized adaptive control scheme constructs a decoupled well-designed reference model first. Then, according to the well-designed model, this paper develops a digital decentralized adaptive tracker based on the optimal analog control and prediction-based digital redesign technique for the sampled-data large-scale coupling system. In order to enhance the tracking performance of the digital tracker at specified sampling instants, we apply the iterative learning control (ILC) to train the control input via continual learning. As a result, the proposed iterative learning-based decentralized adaptive tracker not only has robust closed-loop decoupled property but also possesses good tracking performance at both transient and steady state. Besides, evolutionary programming is applied to search for a good learning gain to speed up the learning process of ILC.

  8. Color Image Denoising via Discriminatively Learned Iterative Shrinkage.

    PubMed

    Sun, Jian; Sun, Jian; Xu, Zingben

    2015-11-01

    In this paper, we propose a novel model, a discriminatively learned iterative shrinkage (DLIS) model, for color image denoising. The DLIS is a generalization of wavelet shrinkage by iteratively performing shrinkage over patch groups and whole image aggregation. We discriminatively learn the shrinkage functions and basis from the training pairs of noisy/noise-free images, which can adaptively handle different noise characteristics in luminance/chrominance channels, and the unknown structured noise in real-captured color images. Furthermore, to remove the splotchy real color noises, we design a Laplacian pyramid-based denoising framework to progressively recover the clean image from the coarsest scale to the finest scale by the DLIS model learned from the real color noises. Experiments show that our proposed approach can achieve the state-of-the-art denoising results on both synthetic denoising benchmark and real-captured color images.

  9. Decentralized Control of Sound Radiation Using Iterative Loop Recovery

    NASA Technical Reports Server (NTRS)

    Schiller, Noah H.; Cabell, Randolph H.; Fuller, Chris R.

    2009-01-01

    A decentralized model-based control strategy is designed to reduce low-frequency sound radiation from periodically stiffened panels. While decentralized control systems tend to be scalable, performance can be limited due to modeling error introduced by the unmodeled interaction between neighboring control units. Since bounds on modeling error are not known in advance, it is difficult to ensure the decentralized control system will be robust without making the controller overly conservative. Therefore an iterative approach is suggested, which utilizes frequency-shaped loop recovery. The approach accounts for modeling error introduced by neighboring control loops, requires no communication between subsystems, and is relatively simple. The control strategy is evaluated numerically using a model of a stiffened aluminum panel that is representative of the sidewall of an aircraft. Simulations demonstrate that the iterative approach can achieve significant reductions in radiated sound power from the stiffened panel without destabilizing neighboring control units.

  10. Decentralized control of sound radiation using iterative loop recovery.

    PubMed

    Schiller, Noah H; Cabell, Randolph H; Fuller, Chris R

    2010-10-01

    A decentralized model-based control strategy is designed to reduce low-frequency sound radiation from periodically stiffened panels. While decentralized control systems tend to be scalable, performance can be limited due to modeling error introduced by the unmodeled interaction between neighboring control units. Since bounds on modeling error are not known in advance, it is difficult to ensure the decentralized control system will be robust without making the controller overly conservative. Therefore an iterative approach is suggested, which utilizes frequency-shaped loop recovery. The approach accounts for modeling error introduced by neighboring control loops, requires no communication between subsystems, and is relatively simple. The control strategy is evaluated numerically using a model of a stiffened aluminum panel that is representative of the sidewall of an aircraft. Simulations demonstrate that the iterative approach can achieve significant reductions in radiated sound power from the stiffened panel without destabilizing neighboring control units.

  11. Iterative LQG Controller Design Through Closed-Loop Identification

    NASA Technical Reports Server (NTRS)

    Hsiao, Min-Hung; Huang, Jen-Kuang; Cox, David E.

    1996-01-01

    This paper presents an iterative Linear Quadratic Gaussian (LQG) controller design approach for a linear stochastic system with an uncertain open-loop model and unknown noise statistics. This approach consists of closed-loop identification and controller redesign cycles. In each cycle, the closed-loop identification method is used to identify an open-loop model and a steady-state Kalman filter gain from closed-loop input/output test data obtained by using a feedback LQG controller designed from the previous cycle. Then the identified open-loop model is used to redesign the state feedback. The state feedback and the identified Kalman filter gain are used to form an updated LQC controller for the next cycle. This iterative process continues until the updated controller converges. The proposed controller design is demonstrated by numerical simulations and experiments on a highly unstable large-gap magnetic suspension system.

  12. EC power management and NTM control in ITER

    NASA Astrophysics Data System (ADS)

    Poli, Francesca; Fredrickson, E.; Henderson, M.; Bertelli, N.; Farina, D.; Figini, L.; Nowak, S.; Poli, E.; Sauter, O.

    2016-10-01

    The suppression of Neoclassical Tearing Modes (NTMs) is an essential requirement for the achievement of the demonstration baseline in ITER. The Electron Cyclotron upper launcher is specifically designed to provide highly localized heating and current drive for NTM stabilization. In order to assess the power management for shared applications, we have performed time-dependent simulations for ITER scenarios covering operation from half to full field. The free-boundary TRANSP simulations evolve the magnetic equilibrium and the pressure profiles in response to the heating and current drive sources and are interfaced with a GRE for the evolution of size and frequency of the magnetic islands. Combined with a feedback control of the EC power and the steering angle, these simulations are used to model the plasma response to NTM control, accounting for the misalignment of the EC deposition with the resonant surfaces, uncertainties in the magnetic equilibrium reconstruction and in the magnetic island detection threshold. Simulations indicate that the threshold for detection of the island should not exceed 2-3cm, that pre-emptive control is a preferable option, and that for safe operation the power needed for NTM control should be reserved, rather than shared with other applications. Work supported by ITER under IO/RFQ/13/9550/JTR and by DOE under DE-AC02-09CH11466.

  13. Upper limb stroke rehabilitation: the effectiveness of Stimulation Assistance through Iterative Learning (SAIL).

    PubMed

    Meadmore, Katie L; Cai, Zhonglun; Tong, Daisy; Hughes, Ann-Marie; Freeman, Chris T; Rogers, Eric; Burridge, Jane H

    2011-01-01

    A novel system has been developed which combines robotic therapy with electrical stimulation (ES) for upper limb stroke rehabilitation. This technology, termed SAIL: Stimulation Assistance through Iterative Learning, employs advanced model-based iterative learning control (ILC) algorithms to precisely assist participant's completion of 3D tracking tasks with their impaired arm. Data is reported from a preliminary study with unimpaired participants, and also from a single hemiparetic stroke participant with reduced upper limb function who has used the system in a clinical trial. All participants completed tasks which involved moving their (impaired) arm to follow an image of a slowing moving sphere along a trajectory. The participants' arm was supported by a robot and ES was applied to the triceps brachii and anterior deltoid muscles. During each task, the same tracking trajectory was repeated 6 times and ILC was used to compute the stimulation signals to be applied on the next iteration. Unimpaired participants took part in a single, one hour training session and the stroke participant undertook 18, 1 hour treatment sessions composed of tracking tasks varying in length, orientation and speed. The results reported describe changes in tracking ability and demonstrate feasibility of the SAIL system for upper limb rehabilitation.

  14. In-vessel dust and tritium control strategy in ITER

    NASA Astrophysics Data System (ADS)

    Shimada, M.; Pitts, R. A.; Ciattaglia, S.; Carpentier, S.; Choi, C. H.; Dell Orco, G.; Hirai, T.; Kukushkin, A.; Lisgo, S.; Palmer, J.; Shu, W.; Veshchev, E.

    2013-07-01

    A baseline strategy for dust and tritium-inventory control and recovery in ITER has been established and preparations are underway for its implementation. Limits on dust and tritium-inventory are an integral part of the ITER safety case and are fixed at 1 kg for tritium, 1000 kg for mobilisable dust and 11 kg (beryllium)/76 kg (tungsten) for dust on hot surfaces. Maximum average T-retention rates of ˜1 g/shot are estimated for baseline inductive operation at QDT = 10, suggesting that the in-vessel T-retention could reach the administrative limit of 640 g in as little as ˜2 months of operation. Baking is expected to remove a significant fraction of the T co-deposited on the divertor targets. Despite large uncertainties, dust quantities are expected to remain well below safety limits over the divertor cassette lifetime. In situ aspiration during divertor cassette exchange is foreseen as the main dust removal technique.

  15. Studying the implementation of iterative impedance control for assistive hand rehabilitation using an exoskeleton.

    PubMed

    Martineau, T; Vaidyanathan, R

    2017-07-01

    A positive training synergy can be obtained when two individuals attempt to learn the same motor task while mechanically coupled to one another. In this paper, we have studied how mimicking this interaction through impedance control can be exploited to improve assistance delivered by hand exoskeleton devices during rehabilitation. In this context, the machine and user take complementary roles akin to two coupled individuals. We present the derivation of a dynamic model of the human hand for the purpose of controller development for new hand exoskeleton platforms. Using this model, we have simulated the behavior of an iterative impedance controller programmed for rehabilitative training. The controller interacts with cylindrical objects to be grasped by means of an inverse kinematic mapping and tuning of mechanical impedance characteristic of the finger joints. Through fusion of concepts from motor control theory, muscle impedance and task oriented control, the controller is capable of iteratively learn to accomplish simple tasks involving grasping and lifting while cooperating with a user. The controller is also capable of adapting to more complex dynamics for more dexterous tasks, such as pulling on a hand-bar or loosening the cap of a jar. We believe the human-robot synergy established in this investigation has benefits to therapy. It can be combined with a broad range of training exercises and represents an incremental step towards mimicking natural human motor responses.

  16. Fixed Point Transformations Based Iterative Control of a Polymerization Reaction

    NASA Astrophysics Data System (ADS)

    Tar, József K.; Rudas, Imre J.

    As a paradigm of strongly coupled non-linear multi-variable dynamic systems the mathematical model of the free-radical polymerization of methyl-metachrylate with azobis (isobutyro-nitrile) as an initiator and toluene as a solvent taking place in a jacketed Continuous Stirred Tank Reactor (CSTR) is considered. In the adaptive control of this system only a single input variable is used as the control signal (the process input, i.e. dimensionless volumetric flow rate of the initiator), and a single output variable is observed (the process output, i.e. the number-average molecular weight of the polymer). Simulation examples illustrate that on the basis of a very rough and primitive model consisting of two scalar variables various fixed-point transformations based convergent iterations result in a novel, sophisticated adaptive control.

  17. Iterative exponential growth of stereo- and sequence-controlled polymers.

    PubMed

    Barnes, Jonathan C; Ehrlich, Deborah J C; Gao, Angela X; Leibfarth, Frank A; Jiang, Yivan; Zhou, Erica; Jamison, Timothy F; Johnson, Jeremiah A

    2015-10-01

    Chemists have long sought sequence-controlled synthetic polymers that mimic nature's biopolymers, but a practical synthetic route that enables absolute control over polymer sequence and structure remains a key challenge. Here, we report an iterative exponential growth plus side-chain functionalization (IEG+) strategy that begins with enantiopure epoxides and facilitates the efficient synthesis of a family of uniform >3 kDa macromolecules of varying sequence and stereoconfiguration that are coupled to produce unimolecular polymers (>6 kDa) with sequences and structures that cannot be obtained using traditional polymerization techniques. Selective side-chain deprotection of three hexadecamers is also demonstrated, which imbues each compound with the ability to dissolve in water. We anticipate that these new macromolecules and the general IEG+ strategy will find broad application as a versatile platform for the scalable synthesis of sequence-controlled polymers.

  18. Iterative exponential growth of stereo- and sequence-controlled polymers

    NASA Astrophysics Data System (ADS)

    Barnes, Jonathan C.; Ehrlich, Deborah J. C.; Gao, Angela X.; Leibfarth, Frank A.; Jiang, Yivan; Zhou, Erica; Jamison, Timothy F.; Johnson, Jeremiah A.

    2015-10-01

    Chemists have long sought sequence-controlled synthetic polymers that mimic nature's biopolymers, but a practical synthetic route that enables absolute control over polymer sequence and structure remains a key challenge. Here, we report an iterative exponential growth plus side-chain functionalization (IEG+) strategy that begins with enantiopure epoxides and facilitates the efficient synthesis of a family of uniform >3 kDa macromolecules of varying sequence and stereoconfiguration that are coupled to produce unimolecular polymers (>6 kDa) with sequences and structures that cannot be obtained using traditional polymerization techniques. Selective side-chain deprotection of three hexadecamers is also demonstrated, which imbues each compound with the ability to dissolve in water. We anticipate that these new macromolecules and the general IEG+ strategy will find broad application as a versatile platform for the scalable synthesis of sequence-controlled polymers.

  19. Error control of iterative linear solvers for integrated groundwater models.

    PubMed

    Dixon, Matthew F; Bai, Zhaojun; Brush, Charles F; Chung, Francis I; Dogrul, Emin C; Kadir, Tariq N

    2011-01-01

    An open problem that arises when using modern iterative linear solvers, such as the preconditioned conjugate gradient method or Generalized Minimum RESidual (GMRES) method, is how to choose the residual tolerance in the linear solver to be consistent with the tolerance on the solution error. This problem is especially acute for integrated groundwater models, which are implicitly coupled to another model, such as surface water models, and resolve both multiple scales of flow and temporal interaction terms, giving rise to linear systems with variable scaling. This article uses the theory of "forward error bound estimation" to explain the correspondence between the residual error in the preconditioned linear system and the solution error. Using examples of linear systems from models developed by the US Geological Survey and the California State Department of Water Resources, we observe that this error bound guides the choice of a practical measure for controlling the error in linear systems. We implemented a preconditioned GMRES algorithm and benchmarked it against the Successive Over-Relaxation (SOR) method, the most widely known iterative solver for nonsymmetric coefficient matrices. With forward error control, GMRES can easily replace the SOR method in legacy groundwater modeling packages, resulting in the overall simulation speedups as large as 7.74×. This research is expected to broadly impact groundwater modelers through the demonstration of a practical and general approach for setting the residual tolerance in line with the solution error tolerance and presentation of GMRES performance benchmarking results.

  20. Process control strategy for ITER central solenoid operation

    NASA Astrophysics Data System (ADS)

    Maekawa, R.; Takami, S.; Iwamoto, A.; Chang, H.-S.; Forgeas, A.; Chalifour, M.

    2016-12-01

    ITER Central Solenoid (CS) pulse operation induces significant flow disturbance in the forced-flow Supercritical Helium (SHe) cooling circuit, which could impact primarily on the operation of cold circulator (SHe centrifugal pump) in Auxiliary Cold Box (ACB). Numerical studies using Venecia®, SUPERMAGNET and 4C have identified reverse flow at the CS module inlet due to the substantial thermal energy deposition at the inner-most winding. To assess the reliable operation of ACB-CS (dedicated ACB for CS), the process analyses have been conducted with a dynamic process simulation model developed by Cryogenic Process REal-time SimulaTor (C-PREST). As implementing process control of hydrodynamic instability, several strategies have been applied to evaluate their feasibility. The paper discusses control strategy to protect the centrifugal type cold circulator/compressor operations and its impact on the CS cooling.

  1. Fuzzy control iterative algorithm for beam uniformity enhancement of gas laser through boundary layer flow

    NASA Astrophysics Data System (ADS)

    Wu, Xu; Wu, Kenan; Jiang, Wenting

    2014-07-01

    Fuzzy control iterative algorithm for beam uniformity enhancement of gas laser through boundary layer flow is presented. The iterative process of the proposed algorithm is controlled by the fuzzy control theory. In each loop, the MSE and the TUI value are evaluated to fuzzily determine which one is relatively greater, leading to different approaches in the iteration. Computer simulation results proved the effectiveness of the proposed method. Gerchberg- Saxton algorithm, profile smoothing algorithm and fuzzy control iterative algorithm are applied to the diffractive optical element design for the correction of laser beam distorted by a Blausius boundary layer. Fuzzy control iterative algorithm leads to better result than Gerchberg-Saxton algorithm and profile smoothing algorithm. Both mean square error and top ununiformity index of the result obtained by fuzzy control iterative algorithm are rather low.

  2. Sawtooth control in JET with ITER relevant low field side resonance ion cyclotron resonance heating and ITER-like wall

    NASA Astrophysics Data System (ADS)

    Graves, J. P.; Lennholm, M.; Chapman, I. T.; Lerche, E.; Reich, M.; Alper, B.; Bobkov, V.; Dumont, R.; Faustin, J. M.; Jacquet, P.; Jaulmes, F.; Johnson, T.; Keeling, D. L.; Liu, Yueqiang; Nicolas, T.; Tholerus, S.; Blackman, T.; Carvalho, I. S.; Coelho, R.; Van Eester, D.; Felton, R.; Goniche, M.; Kiptily, V.; Monakhov, I.; Nave, M. F. F.; Perez von Thun, C.; Sabot, R.; Sozzi, C.; Tsalas, M.

    2015-01-01

    New experiments at JET with the ITER-like wall show for the first time that ITER-relevant low field side resonance first harmonic ion cyclotron resonance heating (ICRH) can be used to control sawteeth that have been initially lengthened by fast particles. In contrast to previous (Graves et al 2012 Nat. Commun. 3 624) high field side resonance sawtooth control experiments undertaken at JET, it is found that the sawteeth of L-mode plasmas can be controlled with less accurate alignment between the resonance layer and the sawtooth inversion radius. This advantage, as well as the discovery that sawteeth can be shortened with various antenna phasings, including dipole, indicates that ICRH is a particularly effective and versatile tool that can be used in future fusion machines for controlling sawteeth. Without sawtooth control, neoclassical tearing modes (NTMs) and locked modes were triggered at very low normalised beta. High power H-mode experiments show the extent to which ICRH can be tuned to control sawteeth and NTMs while simultaneously providing effective electron heating with improved flushing of high Z core impurities. Dedicated ICRH simulations using SELFO, SCENIC and EVE, including wide drift orbit effects, explain why sawtooth control is effective with various antenna phasings and show that the sawtooth control mechanism cannot be explained by enhancement of the magnetic shear. Hybrid kinetic-magnetohydrodynamic stability calculations using MISHKA and HAGIS unravel the optimal sawtooth control regimes in these ITER relevant plasma conditions.

  3. A strategy for sequence control in vinyl polymers via iterative controlled radical cyclization

    PubMed Central

    Hibi, Yusuke; Ouchi, Makoto; Sawamoto, Mitsuo

    2016-01-01

    There is a growing interest in sequence-controlled polymers toward advanced functional materials. However, control of side-chain order for vinyl polymers has been lacking feasibility in the field of polymer synthesis because of the inherent feature of chain-growth propagation. Here we show a general and versatile strategy to control sequence in vinyl polymers through iterative radical cyclization with orthogonally cleavable and renewable bonds. The proposed methodology employs a repetitive and iterative intramolecular cyclization via a radical intermediate in a one-time template with a radical-generating site at one end and an alkene end at the other, each of which is connected to a linker via independently cleavable and renewable bonds. The unique design specifically allowed control of radical addition reaction although inherent chain-growth intermediate (radical species) was used, as well as the iterative cycle and functionalization for resultant side chains, to lead to sequence-controlled vinyl polymers (or oligomers). PMID:26996881

  4. Magnetic Control of Locked Modes in Present Devices and ITER

    NASA Astrophysics Data System (ADS)

    Volpe, F. A.; Sabbagh, S.; Sweeney, R.; Hender, T.; Kirk, A.; La Haye, R. J.; Strait, E. J.; Ding, Y. H.; Rao, B.; Fietz, S.; Maraschek, M.; Frassinetti, L.; in, Y.; Jeon, Y.; Sakakihara, S.

    2014-10-01

    The toroidal phase of non-rotating (``locked'') neoclassical tearing modes was controlled in several devices by means of applied magnetic perturbations. Evidence is presented from various tokamaks (ASDEX Upgrade, DIII-D, JET, J-TEXT, KSTAR), spherical tori (MAST, NSTX) and a reversed field pinch (EXTRAP-T2R). Furthermore, the phase of interchange modes was controlled in the LHD helical device. These results share a common interpretation in terms of torques acting on the mode. Based on this interpretation, it is predicted that control-coil currents will be sufficient to control the phase of locking in ITER. This will be possible both with the internal coils and with the external error-field-correction coils, and might have promising consequences for disruption avoidance (by aiding the electron cyclotron current drive stabilization of locked modes), as well as for spatially distributing heat loads during disruptions. This work was supported in part by the US Department of Energy under DE-SC0008520, DE-FC-02-04ER54698 and DE-AC02-09CH11466.

  5. Nonlinear Burn Control and Operating Point Optimization in ITER

    NASA Astrophysics Data System (ADS)

    Boyer, Mark; Schuster, Eugenio

    2013-10-01

    Control of the fusion power through regulation of the plasma density and temperature will be essential for achieving and maintaining desired operating points in fusion reactors and burning plasma experiments like ITER. In this work, a volume averaged model for the evolution of the density of energy, deuterium and tritium fuel ions, alpha-particles, and impurity ions is used to synthesize a multi-input multi-output nonlinear feedback controller for stabilizing and modulating the burn condition. Adaptive control techniques are used to account for uncertainty in model parameters, including particle confinement times and recycling rates. The control approach makes use of the different possible methods for altering the fusion power, including adjusting the temperature through auxiliary heating, modulating the density and isotopic mix through fueling, and altering the impurity density through impurity injection. Furthermore, a model-based optimization scheme is proposed to drive the system as close as possible to desired fusion power and temperature references. Constraints are considered in the optimization scheme to ensure that, for example, density and beta limits are avoided, and that optimal operation is achieved even when actuators reach saturation. Supported by the NSF CAREER award program (ECCS-0645086).

  6. Remix as Professional Learning: Educators' Iterative Literacy Practice in CLMOOC

    ERIC Educational Resources Information Center

    Smith, Anna; West-Puckett, Stephanie; Cantrill, Christina; Zamora, Mia

    2016-01-01

    The Connected Learning Massive Open Online Collaboration (CLMOOC) is an online professional development experience designed as an openly networked, production-centered, participatory learning collaboration for educators. Addressing the paucity of research that investigates learning processes in MOOC experiences, this paper examines the situated…

  7. Value Iteration Adaptive Dynamic Programming for Optimal Control of Discrete-Time Nonlinear Systems.

    PubMed

    Wei, Qinglai; Liu, Derong; Lin, Hanquan

    2016-03-01

    In this paper, a value iteration adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon undiscounted optimal control problems for discrete-time nonlinear systems. The present value iteration ADP algorithm permits an arbitrary positive semi-definite function to initialize the algorithm. A novel convergence analysis is developed to guarantee that the iterative value function converges to the optimal performance index function. Initialized by different initial functions, it is proven that the iterative value function will be monotonically nonincreasing, monotonically nondecreasing, or nonmonotonic and will converge to the optimum. In this paper, for the first time, the admissibility properties of the iterative control laws are developed for value iteration algorithms. It is emphasized that new termination criteria are established to guarantee the effectiveness of the iterative control laws. Neural networks are used to approximate the iterative value function and compute the iterative control law, respectively, for facilitating the implementation of the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the present method.

  8. Prefrontal cortex and hybrid learning during iterative competitive games

    PubMed Central

    Abe, Hiroshi; Seo, Hyojung; Lee, Daeyeol

    2012-01-01

    Behavioral changes driven by reinforcement and punishment are referred to as simple or model-free reinforcement learning. Animals can also change their behaviors by observing events that are neither appetitive nor aversive, when these events provide new information about payoffs available from alternative actions. This is an example of model-based reinforcement learning, and can be accomplished by incorporating hypothetical reward signals into the value functions for specific actions. Recent neuroimaging and single-neuron recording studies showed that the prefrontal cortex and the striatum are involved not only in reinforcement and punishment, but also in model-based reinforcement learning. We found evidence for both types of learning, and hence hybrid learning, in monkeys during simulated competitive games. In addition, in both the dorsolateral prefrontal cortex and orbitofrontal cortex, individual neurons heterogeneously encoded signals related to actual and hypothetical outcomes from specific actions, suggesting that both areas might contribute to hybrid learning. PMID:22145879

  9. Policy iteration optimal tracking control for chaotic systems by using an adaptive dynamic programming approach

    NASA Astrophysics Data System (ADS)

    Wei, Qing-Lai; Liu, De-Rong; Xu, Yan-Cai

    2015-03-01

    A policy iteration algorithm of adaptive dynamic programming (ADP) is developed to solve the optimal tracking control for a class of discrete-time chaotic systems. By system transformations, the optimal tracking problem is transformed into an optimal regulation one. The policy iteration algorithm for discrete-time chaotic systems is first described. Then, the convergence and admissibility properties of the developed policy iteration algorithm are presented, which show that the transformed chaotic system can be stabilized under an arbitrary iterative control law and the iterative performance index function simultaneously converges to the optimum. By implementing the policy iteration algorithm via neural networks, the developed optimal tracking control scheme for chaotic systems is verified by a simulation. Project supported by the National Natural Science Foundation of China (Grant Nos. 61034002, 61233001, 61273140, 61304086, and 61374105) and the Beijing Natural Science Foundation, China (Grant No. 4132078).

  10. Studio Based Learning: Proposing, Critiquing, Iterating Our Way to Person-Centeredness for Better Classroom Management

    ERIC Educational Resources Information Center

    Brocato, Kay

    2009-01-01

    This article relates how the proposing, critiquing, iterating process of studio-based learning (SBL) provides for person-centered classroom management. SBL is defined in connection to how the pedagogy works within a school of architecture. Then, a description of how the approach is applied to one course in a teacher education program is offered.…

  11. Not All Wizards Are from Oz: Iterative Design of Intelligent Learning Environments by Communication Capacity Tapering

    ERIC Educational Resources Information Center

    Mavrikis, Manolis; Gutierrez-Santos, Sergio

    2010-01-01

    This paper presents a methodology for the design of intelligent learning environments. We recognise that in the educational technology field, theory development and system-design should be integrated and rely on an iterative process that addresses: (a) the difficulty to elicit precise, concise, and operationalized knowledge from "experts" and (b)…

  12. Multimodal and Adaptive Learning Management: An Iterative Design

    ERIC Educational Resources Information Center

    Squires, David R.; Orey, Michael A.

    2015-01-01

    The purpose of this study is to measure the outcome of a comprehensive learning management system implemented at a Spinal Cord Injury (SCI) hospital in the Southeast United States. Specifically this SCI hospital has been experiencing an evident volume of patients returning seeking more information about the nature of their injuries. Recognizing…

  13. Multimodal and Adaptive Learning Management: An Iterative Design

    ERIC Educational Resources Information Center

    Squires, David R.; Orey, Michael A.

    2015-01-01

    The purpose of this study is to measure the outcome of a comprehensive learning management system implemented at a Spinal Cord Injury (SCI) hospital in the Southeast United States. Specifically this SCI hospital has been experiencing an evident volume of patients returning seeking more information about the nature of their injuries. Recognizing…

  14. Near-Optimal Controller for Nonlinear Continuous-Time Systems With Unknown Dynamics Using Policy Iteration.

    PubMed

    Dutta, Samrat; Patchaikani, Prem Kumar; Behera, Laxmidhar

    2016-07-01

    This paper presents a single-network adaptive critic-based controller for continuous-time systems with unknown dynamics in a policy iteration (PI) framework. It is assumed that the unknown dynamics can be estimated using the Takagi-Sugeno-Kang fuzzy model with arbitrary precision. The successful implementation of a PI scheme depends on the effective learning of critic network parameters. Network parameters must stabilize the system in each iteration in addition to approximating the critic and the cost. It is found that the critic updates according to the Hamilton-Jacobi-Bellman formulation sometimes lead to the instability of the closed-loop systems. In the proposed work, a novel critic network parameter update scheme is adopted, which not only approximates the critic at current iteration but also provides feasible solutions that keep the policy stable in the next step of training by combining a Lyapunov-based linear matrix inequalities approach with PI. The critic modeling technique presented here is the first of its kind to address this issue. Though multiple literature exists discussing the convergence of PI, however, to the best of our knowledge, there exists no literature, which focuses on the effect of critic network parameters on the convergence. Computational complexity in the proposed algorithm is reduced to the order of (Fz)(n-1) , where n is the fuzzy state dimensionality and Fz is the number of fuzzy zones in the states space. A genetic algorithm toolbox of MATLAB is used for searching stable parameters while minimizing the training error. The proposed algorithm also provides a way to solve for the initial stable control policy in the PI scheme. The algorithm is validated through real-time experiment on a commercial robotic manipulator. Results show that the algorithm successfully finds stable critic network parameters in real time for a highly nonlinear system.

  15. Quantum-Inspired Multidirectional Associative Memory With a Self-Convergent Iterative Learning.

    PubMed

    Masuyama, Naoki; Loo, Chu Kiong; Seera, Manjeevan; Kubota, Naoyuki

    2017-02-06

    Quantum-inspired computing is an emerging research area, which has significantly improved the capabilities of conventional algorithms. In general, quantum-inspired hopfield associative memory (QHAM) has demonstrated quantum information processing in neural structures. This has resulted in an exponential increase in storage capacity while explaining the extensive memory, and it has the potential to illustrate the dynamics of neurons in the human brain when viewed from quantum mechanics perspective although the application of QHAM is limited as an autoassociation. We introduce a quantum-inspired multidirectional associative memory (QMAM) with a one-shot learning model, and QMAM with a self-convergent iterative learning model (IQMAM) based on QHAM in this paper. The self-convergent iterative learning enables the network to progressively develop a resonance state, from inputs to outputs. The simulation experiments demonstrate the advantages of QMAM and IQMAM, especially the stability to recall reliability.

  16. Active learning framework with iterative clustering for bioimage classification.

    PubMed

    Kutsuna, Natsumaro; Higaki, Takumi; Matsunaga, Sachihiro; Otsuki, Tomoshi; Yamaguchi, Masayuki; Fujii, Hirofumi; Hasezawa, Seiichiro

    2012-01-01

    Advances in imaging systems have yielded a flood of images into the research field. A semi-automated facility can reduce the laborious task of classifying this large number of images. Here we report the development of a novel framework, CARTA (Clustering-Aided Rapid Training Agent), applicable to bioimage classification that facilitates annotation and selection of features. CARTA comprises an active learning algorithm combined with a genetic algorithm and self-organizing map. The framework provides an easy and interactive annotation method and accurate classification. The CARTA framework enables classification of subcellular localization, mitotic phases and discrimination of apoptosis in images of plant and human cells with an accuracy level greater than or equal to annotators. CARTA can be applied to classification of magnetic resonance imaging of cancer cells or multicolour time-course images after surgery. Furthermore, CARTA can support development of customized features for classification, high-throughput phenotyping and application of various classification schemes dependent on the user's purpose.

  17. Linear decentralized learning control

    NASA Technical Reports Server (NTRS)

    Lee, Soo C.; Longman, Richard W.; Phan, Minh

    1992-01-01

    The new field of learning control develops controllers that learn to improve their performance at executing a given task, based on experience performing this task. The simplest forms of learning control are based on the same concept as integral control, but operating in the domain of the repetitions of the task. This paper studies the use of such controllers in a decentralized system, such as a robot with the controller for each link acting independently. The basic result of the paper is to show that stability of the learning controllers for all subsystems when the coupling between subsystems is turned off, assures stability of the decentralized learning in the coupled system, provided that the sample time in the digital learning controller is sufficiently short.

  18. Learning control system design based on 2-D theory - An application to parallel link manipulator

    NASA Technical Reports Server (NTRS)

    Geng, Z.; Carroll, R. L.; Lee, J. D.; Haynes, L. H.

    1990-01-01

    An approach to iterative learning control system design based on two-dimensional system theory is presented. A two-dimensional model for the iterative learning control system which reveals the connections between learning control systems and two-dimensional system theory is established. A learning control algorithm is proposed, and the convergence of learning using this algorithm is guaranteed by two-dimensional stability. The learning algorithm is applied successfully to the trajectory tracking control problem for a parallel link robot manipulator. The excellent performance of this learning algorithm is demonstrated by the computer simulation results.

  19. Indirect decentralized learning control

    NASA Technical Reports Server (NTRS)

    Longman, Richard W.; Lee, Soo C.; Phan, M.

    1992-01-01

    The new field of learning control develops controllers that learn to improve their performance at executing a given task, based on experience performing this specific task. In a previous work, the authors presented a theory of indirect learning control based on use of indirect adaptive control concepts employing simultaneous identification and control. This paper develops improved indirect learning control algorithms, and studies the use of such controllers in decentralized systems. The original motivation of the learning control field was learning in robots doing repetitive tasks such as on an assembly line. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. The basic result of the paper is to show that stability of the indirect learning controllers for all subsystems when the coupling between subsystems is turned off, assures convergence to zero tracking error of the decentralized indirect learning control of the coupled system, provided that the sample time in the digital learning controller is sufficiently short.

  20. Classifying single-trial EEG during motor imagery by iterative spatio-spectral patterns learning (ISSPL).

    PubMed

    Wu, Wei; Gao, Xiaorong; Hong, Bo; Gao, Shangkai

    2008-06-01

    In most current motor-imagery-based brain-computer interfaces (BCIs), machine learning is carried out in two consecutive stages: feature extraction and feature classification. Feature extraction has focused on automatic learning of spatial filters, with little or no attention being paid to optimization of parameters for temporal filters that still require time-consuming, ad hoc manual tuning. In this paper, we present a new algorithm termed iterative spatio-spectral patterns learning (ISSPL) that employs statistical learning theory to perform automatic learning of spatio-spectral filters. In ISSPL, spectral filters and the classifier are simultaneously parameterized for optimization to achieve good generalization performance. A detailed derivation and theoretical analysis of ISSPL are given. Experimental results on two datasets show that the proposed algorithm can correctly identify the discriminative frequency bands, demonstrating the algorithm's superiority over contemporary approaches in classification performance.

  1. The evolution of frequency distributions: relating regularization to inductive biases through iterated learning.

    PubMed

    Reali, Florencia; Griffiths, Thomas L

    2009-06-01

    The regularization of linguistic structures by learners has played a key role in arguments for strong innate constraints on language acquisition, and has important implications for language evolution. However, relating the inductive biases of learners to regularization behavior in laboratory tasks can be challenging without a formal model. In this paper we explore how regular linguistic structures can emerge from language evolution by iterated learning, in which one person's linguistic output is used to generate the linguistic input provided to the next person. We use a model of iterated learning with Bayesian agents to show that this process can result in regularization when learners have the appropriate inductive biases. We then present three experiments demonstrating that simulating the process of language evolution in the laboratory can reveal biases towards regularization that might not otherwise be obvious, allowing weak biases to have strong effects. The results of these experiments suggest that people tend to regularize inconsistent word-meaning mappings, and that even a weak bias towards regularization can allow regular languages to be produced via language evolution by iterated learning.

  2. Scenario-based fitted Q-iteration for adaptive control of water reservoir systems under uncertainty

    NASA Astrophysics Data System (ADS)

    Bertoni, Federica; Giuliani, Matteo; Castelletti, Andrea

    2017-04-01

    Over recent years, mathematical models have largely been used to support planning and management of water resources systems. Yet, the increasing uncertainties in their inputs - due to increased variability in the hydrological regimes - are a major challenge to the optimal operations of these systems. Such uncertainty, boosted by projected changing climate, violates the stationarity principle generally used for describing hydro-meteorological processes, which assumes time persisting statistical characteristics of a given variable as inferred by historical data. As this principle is unlikely to be valid in the future, the probability density function used for modeling stochastic disturbances (e.g., inflows) becomes an additional uncertain parameter of the problem, which can be described in a deterministic and set-membership based fashion. This study contributes a novel method for designing optimal, adaptive policies for controlling water reservoir systems under climate-related uncertainty. The proposed method, called scenario-based Fitted Q-Iteration (sFQI), extends the original Fitted Q-Iteration algorithm by enlarging the state space to include the space of the uncertain system's parameters (i.e., the uncertain climate scenarios). As a result, sFQI embeds the set-membership uncertainty of the future inflow scenarios in the action-value function and is able to approximate, with a single learning process, the optimal control policy associated to any scenario included in the uncertainty set. The method is demonstrated on a synthetic water system, consisting of a regulated lake operated for ensuring reliable water supply to downstream users. Numerical results show that the sFQI algorithm successfully identifies adaptive solutions to operate the system under different inflow scenarios, which outperform the control policy designed under historical conditions. Moreover, the sFQI policy generalizes over inflow scenarios not directly experienced during the policy design

  3. Iterative volume morphing and learning for mobile tumor based on 4DCT

    NASA Astrophysics Data System (ADS)

    Mao, Songan; Wu, Huanmei; Sandison, George; Fang, Shiaofen

    2017-02-01

    During image-guided cancer radiation treatment, three-dimensional (3D) tumor volumetric information is important for treatment success. However, it is typically not feasible to image a patient’s 3D tumor continuously in real time during treatment due to concern over excessive patient radiation dose. We present a new iterative morphing algorithm to predict the real-time 3D tumor volume based on time-resolved computed tomography (4DCT) acquired before treatment. An offline iterative learning process has been designed to derive a target volumetric deformation function from one breathing phase to another. Real-time volumetric prediction is performed to derive the target 3D volume during treatment delivery. The proposed iterative deformable approach for tumor volume morphing and prediction based on 4DCT is innovative because it makes three major contributions: (1) a novel approach to landmark selection on 3D tumor surfaces using a minimum bounding box; (2) an iterative morphing algorithm to generate the 3D tumor volume using mapped landmarks; and (3) an online tumor volume prediction strategy based on previously trained deformation functions utilizing 4DCT. The experimental performance showed that the maximum morphing deviations are 0.27% and 1.25% for original patient data and artificially generated data, which is promising. This newly developed algorithm and implementation will have important applications for treatment planning, dose calculation and treatment validation in cancer radiation treatment.

  4. Iterative volume morphing and learning for mobile tumor based on 4DCT.

    PubMed

    Mao, Songan; Wu, Huanmei; Sandison, George; Fang, Shiaofen

    2017-02-21

    During image-guided cancer radiation treatment, three-dimensional (3D) tumor volumetric information is important for treatment success. However, it is typically not feasible to image a patient's 3D tumor continuously in real time during treatment due to concern over excessive patient radiation dose. We present a new iterative morphing algorithm to predict the real-time 3D tumor volume based on time-resolved computed tomography (4DCT) acquired before treatment. An offline iterative learning process has been designed to derive a target volumetric deformation function from one breathing phase to another. Real-time volumetric prediction is performed to derive the target 3D volume during treatment delivery. The proposed iterative deformable approach for tumor volume morphing and prediction based on 4DCT is innovative because it makes three major contributions: (1) a novel approach to landmark selection on 3D tumor surfaces using a minimum bounding box; (2) an iterative morphing algorithm to generate the 3D tumor volume using mapped landmarks; and (3) an online tumor volume prediction strategy based on previously trained deformation functions utilizing 4DCT. The experimental performance showed that the maximum morphing deviations are 0.27% and 1.25% for original patient data and artificially generated data, which is promising. This newly developed algorithm and implementation will have important applications for treatment planning, dose calculation and treatment validation in cancer radiation treatment.

  5. Analysis of Distribution of Time Scores in Iterative Learning Type Courseware Using Fourier Transform

    NASA Astrophysics Data System (ADS)

    Watanabe, Hiroyuki

    In this research, an iterative learning type courseware was made, the distribution of time scores in the courseware is gotten by the learning management system. It is a proposed method by which the distribution of time scores is changed to frequency and to power spectrum using Fourier Transform. The learning process continues until students get the passing scores and are classified by using these values, which are related to average time and the average of scores‧ square. Furthermore, the cross-correlation coefficients between the standard student and students are calculated, and delay times are analyzed. Finally, the transfer functions of some students are calculated, and the characteristics of the learning processes are analyzed.

  6. A policy iteration approach to online optimal control of continuous-time constrained-input systems.

    PubMed

    Modares, Hamidreza; Naghibi Sistani, Mohammad-Bagher; Lewis, Frank L

    2013-09-01

    This paper is an effort towards developing an online learning algorithm to find the optimal control solution for continuous-time (CT) systems subject to input constraints. The proposed method is based on the policy iteration (PI) technique which has recently evolved as a major technique for solving optimal control problems. Although a number of online PI algorithms have been developed for CT systems, none of them take into account the input constraints caused by actuator saturation. In practice, however, ignoring these constraints leads to performance degradation or even system instability. In this paper, to deal with the input constraints, a suitable nonquadratic functional is employed to encode the constraints into the optimization formulation. Then, the proposed PI algorithm is implemented on an actor-critic structure to solve the Hamilton-Jacobi-Bellman (HJB) equation associated with this nonquadratic cost functional in an online fashion. That is, two coupled neural network (NN) approximators, namely an actor and a critic are tuned online and simultaneously for approximating the associated HJB solution and computing the optimal control policy. The critic is used to evaluate the cost associated with the current policy, while the actor is used to find an improved policy based on information provided by the critic. Convergence to a close approximation of the HJB solution as well as stability of the proposed feedback control law are shown. Simulation results of the proposed method on a nonlinear CT system illustrate the effectiveness of the proposed approach.

  7. Nuclear Safety Functions of ITER Gas Injection System Instrumentation and Control and the Concept Design

    NASA Astrophysics Data System (ADS)

    Yang, Yu; Maruyama, S.; Fossen, A.; Villers, F.; Kiss, G.; Zhang, Bo; Li, Bo; Jiang, Tao; Huang, Xiangmei

    2016-08-01

    The ITER Gas Injection System (GIS) plays an important role on fueling, wall conditioning and distribution for plasma operation. Besides that, to support the safety function of ITER, GIS needs to implement three nuclear safety Instrumentation and Control (I&C) functions. In this paper, these three functions are introduced with the emphasis on their latest safety classifications. The nuclear I&C design concept is briefly discussed at the end.

  8. Iterative Development of Visual Control Systems in a Research Vivarium

    PubMed Central

    Bassuk, James A.; Washington, Ida M.

    2014-01-01

    The goal of this study was to test the hypothesis that reintroduction of Continuous Performance Improvement (CPI) methodology, a lean approach to management at Seattle Children’s (Hospital, Research Institute, Foundation), would facilitate engagement of vivarium employees in the development and sustainment of a daily management system and a work-in-process board. Such engagement was implemented through reintroduction of aspects of the Toyota Production System. Iterations of a Work-In-Process Board were generated using Shewhart’s Plan-Do-Check-Act process improvement cycle. Specific attention was given to the importance of detecting and preventing errors through assessment of the following 5 levels of quality: Level 1, customer inspects; Level 2, company inspects; Level 3, work unit inspects; Level 4, self-inspection; Level 5, mistake proofing. A functioning iteration of a Mouse Cage Work-In-Process Board was eventually established using electronic data entry, an improvement that increased the quality level from 1 to 3 while reducing wasteful steps, handoffs and queues. A visual workplace was realized via a daily management system that included a Work-In-Process Board, a problem solving board and two Heijunka boards. One Heijunka board tracked cage changing as a function of a biological kanban, which was validated via ammonia levels. A 17% reduction in cage changing frequency provided vivarium staff with additional time to support Institute researchers in their mutual goal of advancing cures for pediatric diseases. Cage washing metrics demonstrated an improvement in the flow continuum in which a traditional batch and queue push system was replaced with a supermarket-type pull system. Staff engagement during the improvement process was challenging and is discussed. The collective data indicate that the hypothesis was found to be true. The reintroduction of CPI into daily work in the vivarium is consistent with the 4P Model of the Toyota Way and selected

  9. Iterative development of visual control systems in a research vivarium.

    PubMed

    Bassuk, James A; Washington, Ida M

    2014-01-01

    The goal of this study was to test the hypothesis that reintroduction of Continuous Performance Improvement (CPI) methodology, a lean approach to management at Seattle Children's (Hospital, Research Institute, Foundation), would facilitate engagement of vivarium employees in the development and sustainment of a daily management system and a work-in-process board. Such engagement was implemented through reintroduction of aspects of the Toyota Production System. Iterations of a Work-In-Process Board were generated using Shewhart's Plan-Do-Check-Act process improvement cycle. Specific attention was given to the importance of detecting and preventing errors through assessment of the following 5 levels of quality: Level 1, customer inspects; Level 2, company inspects; Level 3, work unit inspects; Level 4, self-inspection; Level 5, mistake proofing. A functioning iteration of a Mouse Cage Work-In-Process Board was eventually established using electronic data entry, an improvement that increased the quality level from 1 to 3 while reducing wasteful steps, handoffs and queues. A visual workplace was realized via a daily management system that included a Work-In-Process Board, a problem solving board and two Heijunka boards. One Heijunka board tracked cage changing as a function of a biological kanban, which was validated via ammonia levels. A 17% reduction in cage changing frequency provided vivarium staff with additional time to support Institute researchers in their mutual goal of advancing cures for pediatric diseases. Cage washing metrics demonstrated an improvement in the flow continuum in which a traditional batch and queue push system was replaced with a supermarket-type pull system. Staff engagement during the improvement process was challenging and is discussed. The collective data indicate that the hypothesis was found to be true. The reintroduction of CPI into daily work in the vivarium is consistent with the 4P Model of the Toyota Way and selected Principles

  10. Learning to Teach Elementary Science Through Iterative Cycles of Enactment in Culturally and Linguistically Diverse Contexts

    NASA Astrophysics Data System (ADS)

    Bottoms, SueAnn I.; Ciechanowski, Kathryn M.; Hartman, Brian

    2015-12-01

    Iterative cycles of enactment embedded in culturally and linguistically diverse contexts provide rich opportunities for preservice teachers (PSTs) to enact core practices of science. This study is situated in the larger Families Involved in Sociocultural Teaching and Science, Technology, Engineering and Mathematics (FIESTAS) project, which weaves together cycles of enactment, core practices in science education and culturally relevant pedagogies. The theoretical foundation draws upon situated learning theory and communities of practice. Using video analysis by PSTs and course artifacts, the authors studied how the iterative process of these cycles guided PSTs development as teachers of elementary science. Findings demonstrate how PSTs were drawing on resources to inform practice, purposefully noticing their practice, renegotiating their roles in teaching, and reconsidering "professional blindness" through cultural practice.

  11. A theoretical analysis of temporal difference learning in the iterated prisoner's dilemma game.

    PubMed

    Masuda, Naoki; Ohtsuki, Hisashi

    2009-11-01

    Direct reciprocity is a chief mechanism of mutual cooperation in social dilemma. Agents cooperate if future interactions with the same opponents are highly likely. Direct reciprocity has been explored mostly by evolutionary game theory based on natural selection. Our daily experience tells, however, that real social agents including humans learn to cooperate based on experience. In this paper, we analyze a reinforcement learning model called temporal difference learning and study its performance in the iterated Prisoner's Dilemma game. Temporal difference learning is unique among a variety of learning models in that it inherently aims at increasing future payoffs, not immediate ones. It also has a neural basis. We analytically and numerically show that learners with only two internal states properly learn to cooperate with retaliatory players and to defect against unconditional cooperators and defectors. Four-state learners are more capable of achieving a high payoff against various opponents. Moreover, we numerically show that four-state learners can learn to establish mutual cooperation for sufficiently small learning rates.

  12. Robust model predictive control by iterative optimisation for polytopic uncertain systems

    NASA Astrophysics Data System (ADS)

    Wang, Chuanxu

    2012-09-01

    This article addresses robust model predictive control (MPC) for constrained systems with polytopic uncertainty description. Firstly, in the technique which parametrises the infinite horizon control moves into a single state feedback law and invokes the parameter-dependent Lyapunov method for achieving closed-loop stability, the slack matrices are iteratively solved at each sampling time. Secondly, in the technique which parametrises the infinite horizon control moves into a set of free perturbations followed by a single state feedback law, the feedback gains within the switch horizon are iteratively found at each sampling time, rather than just inherited from the previous sampling time. Numerical examples show that iterative MPC can not only improve the control performance, but also enlarge the region of attraction.

  13. Efficient decentralized iterative learning tracker for unknown sampled-data interconnected large-scale state-delay system with closed-loop decoupling property.

    PubMed

    Tsai, Jason Sheng-Hong; Chen, Fu-Ming; Yu, Tze-Yu; Guo, Shu-Mei; Shieh, Leang-San

    2012-01-01

    In this paper, an efficient decentralized iterative learning tracker is proposed to improve the dynamic performance of the unknown controllable and observable sampled-data interconnected large-scale state-delay system, which consists of N multi-input multi-output (MIMO) subsystems, with the closed-loop decoupling property. The off-line observer/Kalman filter identification (OKID) method is used to obtain the decentralized linear models for subsystems in the interconnected large-scale system. In order to get over the effect of modeling error on the identified linear model of each subsystem, an improved observer with the high-gain property based on the digital redesign approach is developed to replace the observer identified by OKID. Then, the iterative learning control (ILC) scheme is integrated with the high-gain tracker design for the decentralized models. To significantly reduce the iterative learning epochs, a digital-redesign linear quadratic digital tracker with the high-gain property is proposed as the initial control input of ILC. The high-gain property controllers can suppress uncertain errors such as modeling errors, nonlinear perturbations, and external disturbances (Guo et al., 2000) [18]. Thus, the system output can quickly and accurately track the desired reference in one short time interval after all drastically-changing points of the specified reference input with the closed-loop decoupling property.

  14. An iterative algorithm combining model reduction and control design

    NASA Technical Reports Server (NTRS)

    Hsieh, C.; Kim, J. H.; Zhu, G.; Liu, K.; Skelton, R. E.

    1990-01-01

    A design strategy which integrates model reduction by modal cost analysis and a multiobjective controller design is proposed. The necessary modeling and control algorithms are easily programmed in Matlab standard software. Hence, this method is very practical for controller design for large space structures. The design algorithm also solves the very important problem of tuning multiple loop controllers (multi-input, multi-output, or MIMO). Instead of the single gain change that is used in standard root locus and gain and phase margin theories, this method tunes multiple loop controllers from low to high gain in a systematic way in the design procedure. This design strategy is applied to NASA's Mini-Mast system.

  15. An iterative algorithm combining model reduction and control design

    NASA Technical Reports Server (NTRS)

    Hsieh, C.; Kim, J. H.; Zhu, G.; Liu, K.; Skelton, R. E.

    1990-01-01

    A design strategy which integrates model reduction by modal cost analysis and a multiobjective controller design is proposed. The necessary modeling and control algorithms are easily programmed in Matlab standard software. Hence, this method is very practical for controller design for large space structures. The design algorithm also solves the very important problem of tuning multiple loop controllers (multi-input, multi-output, or MIMO). Instead of the single gain change that is used in standard root locus and gain and phase margin theories, this method tunes multiple loop controllers from low to high gain in a systematic way in the design procedure. This design strategy is applied to NASA's Mini-Mast system.

  16. US NDC Modernization Iteration E1 Prototyping Report: Processing Control Framework

    SciTech Connect

    Prescott, Ryan; Hamlet, Benjamin R.

    2014-12-01

    During the first iteration of the US NDC Modernization Elaboration phase (E1), the SNL US NDC modernization project team developed an initial survey of applicable COTS solutions, and established exploratory prototyping related to the processing control framework in support of system architecture definition. This report summarizes these activities and discusses planned follow-on work.

  17. Learning Iteration-wise Generalized Shrinkage-Thresholding Operators for Blind Deconvolution.

    PubMed

    Wangmeng Zuo; Dongwei Ren; Zhang, David; Shuhang Gu; Lei Zhang

    2016-04-01

    Salient edge selection and time-varying regularization are two crucial techniques to guarantee the success of maximum a posteriori (MAP)-based blind deconvolution. However, the existing approaches usually rely on carefully designed regularizers and handcrafted parameter tuning to obtain satisfactory estimation of the blur kernel. Many regularizers exhibit the structure-preserving smoothing capability, but fail to enhance salient edges. In this paper, under the MAP framework, we propose the iteration-wise ℓp-norm regularizers together with data-driven strategy to address these issues. First, we extend the generalized shrinkage-thresholding (GST) operator for ℓp-norm minimization with negative p value, which can sharpen salient edges while suppressing trivial details. Then, the iteration-wise GST parameters are specified to allow dynamical salient edge selection and time-varying regularization. Finally, instead of handcrafted tuning, a principled discriminative learning approach is proposed to learn the iterationwise GST operators from the training dataset. Furthermore, the multi-scale scheme is developed to improve the efficiency of the algorithm. Experimental results show that, negative p value is more effective in estimating the coarse shape of blur kernel at the early stage, and the learned GST operators can be well generalized to other dataset and real world blurry images. Compared with the state-of-the-art methods, our method achieves better deblurring results in terms of both quantitative metrics and visual quality, and it is much faster than the state-of-the-art patch-based blind deconvolution method.

  18. e-Learning Application for Machine Maintenance Process using Iterative Method in XYZ Company

    NASA Astrophysics Data System (ADS)

    Nurunisa, Suaidah; Kurniawati, Amelia; Pramuditya Soesanto, Rayinda; Yunan Kurnia Septo Hediyanto, Umar

    2016-02-01

    XYZ Company is a company based on manufacturing part for airplane, one of the machine that is categorized as key facility in the company is Millac 5H6P. As a key facility, the machines should be assured to work well and in peak condition, therefore, maintenance process is needed periodically. From the data gathering, it is known that there are lack of competency from the maintenance staff to maintain different type of machine which is not assigned by the supervisor, this indicate that knowledge which possessed by maintenance staff are uneven. The purpose of this research is to create knowledge-based e-learning application as a realization from externalization process in knowledge transfer process to maintain the machine. The application feature are adjusted for maintenance purpose using e-learning framework for maintenance process, the content of the application support multimedia for learning purpose. QFD is used in this research to understand the needs from user. The application is built using moodle with iterative method for software development cycle and UML Diagram. The result from this research is e-learning application as sharing knowledge media for maintenance staff in the company. From the test, it is known that the application make maintenance staff easy to understand the competencies.

  19. Co-Simulation Research of the Mechanical-Hydraulic-Control Coupling System of ITER Tractor

    NASA Astrophysics Data System (ADS)

    Yang, Xiuqing; Luo, Minzhou; Mei, Tao; Yao, Damao

    2009-06-01

    The virtual prototyping models of the mechanical, hydraulic and control system of the ITER tractor were built with CATIA, ADAMS and MATLAB/Simulink respectively according to its heavy load and high precision characteristics, and the data transfer between the different models was accomplished by the integration interface between different software. Consequently the virtual experimental platform for the multi-disciplinary co-simulation was established. A co-simulation study of the mechanical-hydraulic-control coupling system of the ITER tractor was carried out. The synchronization servo control of parallel hydraulic cylinders was implemented, and the tracking control of the preconcerted trajectory of the hydraulic cylinders was realized on the established experimental platform. This paper presents the optimization design and technology rebuilding for the complicated coupling system with its theoretic foundation and co-simulation virtual experimental platform.

  20. An iterative symplectic pseudospectral method to solve nonlinear state-delayed optimal control problems

    NASA Astrophysics Data System (ADS)

    Peng, Haijun; Wang, Xinwei; Zhang, Sheng; Chen, Biaosong

    2017-07-01

    Nonlinear state-delayed optimal control problems have complex nonlinear characters. To solve this complex nonlinear problem, an iterative symplectic pseudospectral method based on quasilinearization techniques, the dual variational principle and pseudospectral methods is proposed in this paper. First, the proposed method transforms the original nonlinear optimal control problem into a series of linear quadratic optimal control problems. Then, a symplectic pseudospectral method is developed to solve these converted linear quadratic state-delayed optimal control problems. Coefficient matrices in the proposed method are sparse and symmetric since the dual variational principle is used, which makes the proposed method highly efficient. Converged numerical solutions with high precision can be obtained after a few iterations due to the benefit of the local pseudospectral method and quasilinearization techniques. In the numerical simulations, other numerical methods were used for comparisons. The numerical simulation results show that the proposed method is highly accurate, efficient and robust.

  1. Ground Truth Creation for Complex Clinical NLP Tasks - an Iterative Vetting Approach and Lessons Learned.

    PubMed

    Liang, Jennifer J; Tsou, Ching-Huei; Devarakonda, Murthy V

    2017-01-01

    Natural language processing (NLP) holds the promise of effectively analyzing patient record data to reduce cognitive load on physicians and clinicians in patient care, clinical research, and hospital operations management. A critical need in developing such methods is the "ground truth" dataset needed for training and testing the algorithms. Beyond localizable, relatively simple tasks, ground truth creation is a significant challenge because medical experts, just as physicians in patient care, have to assimilate vast amounts of data in EHR systems. To mitigate potential inaccuracies of the cognitive challenges, we present an iterative vetting approach for creating the ground truth for complex NLP tasks. In this paper, we present the methodology, and report on its use for an automated problem list generation task, its effect on the ground truth quality and system accuracy, and lessons learned from the effort.

  2. Ground Truth Creation for Complex Clinical NLP Tasks – an Iterative Vetting Approach and Lessons Learned

    PubMed Central

    Liang, Jennifer J.; Tsou, Ching-Huei; Devarakonda, Murthy V.

    2017-01-01

    Natural language processing (NLP) holds the promise of effectively analyzing patient record data to reduce cognitive load on physicians and clinicians in patient care, clinical research, and hospital operations management. A critical need in developing such methods is the “ground truth” dataset needed for training and testing the algorithms. Beyond localizable, relatively simple tasks, ground truth creation is a significant challenge because medical experts, just as physicians in patient care, have to assimilate vast amounts of data in EHR systems. To mitigate potential inaccuracies of the cognitive challenges, we present an iterative vetting approach for creating the ground truth for complex NLP tasks. In this paper, we present the methodology, and report on its use for an automated problem list generation task, its effect on the ground truth quality and system accuracy, and lessons learned from the effort. PMID:28815130

  3. Single-Iteration Learning Algorithm for Feed-Forward Neural Networks

    SciTech Connect

    Barhen, J.; Cogswell, R.; Protopopescu, V.

    1999-07-31

    A new methodology for neural learning is presented, whereby only a single iteration is required to train a feed-forward network with near-optimal results. To this aim, a virtual input layer is added to the multi-layer architecture. The virtual input layer is connected to the nominal input layer by a specird nonlinear transfer function, and to the fwst hidden layer by regular (linear) synapses. A sequence of alternating direction singular vrdue decompositions is then used to determine precisely the inter-layer synaptic weights. This algorithm exploits the known separability of the linear (inter-layer propagation) and nonlinear (neuron activation) aspects of information &ansfer within a neural network.

  4. Policy Iteration for H∞ Optimal Control of Polynomial Nonlinear Systems via Sum of Squares Programming.

    PubMed

    Zhu, Yuanheng; Zhao, Dongbin; Yang, Xiong; Zhang, Qichao

    2017-01-10

    Sum of squares (SOS) polynomials have provided a computationally tractable way to deal with inequality constraints appearing in many control problems. It can also act as an approximator in the framework of adaptive dynamic programming. In this paper, an approximate solution to the H∞ optimal control of polynomial nonlinear systems is proposed. Under a given attenuation coefficient, the Hamilton-Jacobi-Isaacs equation is relaxed to an optimization problem with a set of inequalities. After applying the policy iteration technique and constraining inequalities to SOS, the optimization problem is divided into a sequence of feasible semidefinite programming problems. With the converged solution, the attenuation coefficient is further minimized to a lower value. After iterations, approximate solutions to the smallest L₂-gain and the associated H∞ optimal controller are obtained. Four examples are employed to verify the effectiveness of the proposed algorithm.

  5. Foucauldian Iterative Learning Conversations--An Example of Organisational Change: Developing Conjoint-Work between EPS and Social Workers

    ERIC Educational Resources Information Center

    Apter, Brian

    2014-01-01

    An organisational change-process in a UK local authority (LA) over two years is examined using transcribed excerpts from three meetings. The change-process is analysed using a Foucauldian analytical tool--Iterative Learning Conversations (ILCS). An Educational Psychology Service was changed from being primarily an education-focussed…

  6. Foucauldian Iterative Learning Conversations--An Example of Organisational Change: Developing Conjoint-Work between EPS and Social Workers

    ERIC Educational Resources Information Center

    Apter, Brian

    2014-01-01

    An organisational change-process in a UK local authority (LA) over two years is examined using transcribed excerpts from three meetings. The change-process is analysed using a Foucauldian analytical tool--Iterative Learning Conversations (ILCS). An Educational Psychology Service was changed from being primarily an education-focussed…

  7. How to Combine Objectives and Methods of Evaluation in Iterative ILE Design: Lessons Learned from Designing Ambre-Add

    ERIC Educational Resources Information Center

    Nogry, S.; Jean-Daubias, S.; Guin, N.

    2012-01-01

    This article deals with evaluating an interactive learning environment (ILE) during the iterative-design process. Various aspects of the system must be assessed and a number of evaluation methods are available. In designing the ILE Ambre-add, several techniques were combined to test and refine the system. In particular, we point out the merits of…

  8. Control of particle and power exhaust in pellet fuelled ITER DT scenarios employing integrated models

    NASA Astrophysics Data System (ADS)

    Wiesen, S.; Köchl, F.; Belo, P.; Kotov, V.; Loarte, A.; Parail, V.; Corrigan, G.; Garzotti, L.; Harting, D.

    2017-07-01

    The integrated model JINTRAC is employed to assess the dynamic density evolution of the ITER baseline scenario when fuelled by discrete pellets. The consequences on the core confinement properties, α-particle heating due to fusion and the effect on the ITER divertor operation, taking into account the material limitations on the target heat loads, are discussed within the integrated model. Using the model one can observe that stable but cyclical operational regimes can be achieved for a pellet-fuelled ITER ELMy H-mode scenario with Q  =  10 maintaining partially detached conditions in the divertor. It is shown that the level of divertor detachment is inversely correlated with the core plasma density due to α-particle heating, and thus depends on the density evolution cycle imposed by pellet ablations. The power crossing the separatrix to be dissipated depends on the enhancement of the transport in the pedestal region being linked with the pressure gradient evolution after pellet injection. The fuelling efficacy of the deposited pellet material is strongly dependent on the E  ×  B plasmoid drift. It is concluded that integrated models like JINTRAC, if validated and supported by realistic physics constraints, may help to establish suitable control schemes of particle and power exhaust in burning ITER DT-plasma scenarios.

  9. Infinite horizon self-learning optimal control of nonaffine discrete-time nonlinear systems.

    PubMed

    Wei, Qinglai; Liu, Derong; Yang, Xiong

    2015-04-01

    In this paper, a novel iterative adaptive dynamic programming (ADP)-based infinite horizon self-learning optimal control algorithm, called generalized policy iteration algorithm, is developed for nonaffine discrete-time (DT) nonlinear systems. Generalized policy iteration algorithm is a general idea of interacting policy and value iteration algorithms of ADP. The developed generalized policy iteration algorithm permits an arbitrary positive semidefinite function to initialize the algorithm, where two iteration indices are used for policy improvement and policy evaluation, respectively. It is the first time that the convergence, admissibility, and optimality properties of the generalized policy iteration algorithm for DT nonlinear systems are analyzed. Neural networks are used to implement the developed algorithm. Finally, numerical examples are presented to illustrate the performance of the developed algorithm.

  10. An iterative approach to the optimal co-design of linear control systems

    NASA Astrophysics Data System (ADS)

    Jiang, Yu; Wang, Yebin; Bortoff, Scott A.; Jiang, Zhong-Ping

    2016-04-01

    This paper investigates the optimal co-design of both physical plants and control policies for a class of continuous-time linear control systems. The optimal co-design of a specific linear control system is commonly formulated as a nonlinear non-convex optimisation problem (NNOP), and solved by using iterative techniques, where the plant parameters and the control policy are updated iteratively and alternately. This paper proposes a novel iterative approach to solve the NNOP, where the plant parameters are updated by solving a standard semi-definite programming problem, with non-convexity no longer involved. The proposed system design is generally less conservative in terms of the system performance compared to the conventional system-equivalence-based design, albeit the range of applicability is slightly reduced. A practical optimisation algorithm is proposed to compute a sub-optimal solution ensuring the system stability, and the convergence of the algorithm is established. The effectiveness of the proposed algorithm is illustrated by its application to the optimal co-design of a physical load positioning system.

  11. An iterative investigation into the implementation of handheld computers as learning tools in a science museum

    NASA Astrophysics Data System (ADS)

    Phipps, Molly E.

    In this study I discuss the state of Free-Choice Learning research, and an investigation into the use of personal ubiquitous technology on visitors' experiences at a science center. The three manuscripts included in this document: (1) Review published research on free-choice learning from 1997-2007 from selected journals (2) Examine visitors' interest in using handheld computers (iPods) for learning in a science museum, and report on refining protocols for this type of research. (3) Investigate the impact of using an iPod with supplementary videos on visitors use and understanding of an exhibit on scientific chaos. This study was approached in two phases, the first phase follows the principles of design research in exploring ways to present the iPods within the most favorable context to encourage learning. These changes were systematically implemented and their impact on visitors' experiences were documented. The second phase of the research focused on one particular exhibit and three accompanying videos on the iPod. This exhibit is well loved, but difficult to understand for visitors and docents alike. Through naturalistic inquiry and iterative open coding, I found visitors interpreted appropriate use of the exhibit in four distinct ways: HOW DOES IT WORK?, WAITING FOR THE SPLASH, INTERACTING, and RESTING. However, iPod users all interpreted appropriate use of the exhibit as HOW DOES IT WORK?. Careful observation of visitors' actions at the "Chaos Wheel" exhibit suggests that the exhibit needs some revision if it is to become more accessible to more visitors. The iPod represents one way to increase the accessibility of the exhibit, but other means should be explored.

  12. Iterated non-linear model predictive control based on tubes and contractive constraints.

    PubMed

    Murillo, M; Sánchez, G; Giovanini, L

    2016-05-01

    This paper presents a predictive control algorithm for non-linear systems based on successive linearizations of the non-linear dynamic around a given trajectory. A linear time varying model is obtained and the non-convex constrained optimization problem is transformed into a sequence of locally convex ones. The robustness of the proposed algorithm is addressed adding a convex contractive constraint. To account for linearization errors and to obtain more accurate results an inner iteration loop is added to the algorithm. A simple methodology to obtain an outer bounding-tube for state trajectories is also presented. The convergence of the iterative process and the stability of the closed-loop system are analyzed. The simulation results show the effectiveness of the proposed algorithm in controlling a quadcopter type unmanned aerial vehicle.

  13. Electron Cyclotron power management for control of Neoclassical Tearing Modes in the ITER baseline scenario

    DOE PAGES

    Poli, Francesca M.; Fredrickson, Eric; Henderson, Mark A.; ...

    2017-09-21

    Time-dependent simulations are used to evolve plasma discharges in combination with a Modified Rutherford equation (MRE) for calculation of Neoclassical Tearing Mode (NTM) stability in response to Electron Cyclotron (EC) feedback control in ITER. The main application of this integrated approach is to support the development of control algorithms by analyzing the plasma response with physics-based models and to assess how uncertainties in the detection of the magnetic island and in the EC alignment affect the ability of the ITER EC system to fulfill its purpose. These simulations indicate that it is critical to detect the island as soon asmore » possible, before its size exceeds the EC deposition width, and that maintaining alignment with the rational surface within half of the EC deposition width is needed for stabilization and suppression of the modes, especially in the case of modes with helicity (2,1). A broadening of the deposition profile, for example due to wave scattering by turbulence fluctuations or not well aligned beams, could even be favorable in the case of the (2,1)-NTM, by relaxing an over-focussing of the EC beam and improving the stabilization at the mode onset. Pre-emptive control reduces the power needed for suppression and stabilization in the ITER baseline discharge to a maximum of 5 MW, which should be reserved and available to the Upper Launcher during the entire flattop phase. By assuming continuous triggering of NTMs, with pre-emptive control ITER would be still able to demonstrate a fusion gain of Q=10.« less

  14. The emergence of duality of patterning through iterated learning: Precursors to phonology in a visual lexicon

    PubMed Central

    Giudice, Alex Del

    2012-01-01

    Duality of Patterning, one of Hockett’s (1960) proposed design features unique to human language, refers in part to the arrangements of a relatively small stock of distinguishable meaningless sounds which are combined to create a potentially infinite set of morphemes. Literature regarding the emergence of this design feature is less abundant than that exploring other levels of structure as focus is more often given to the emergence of syntax. In an effort to explore where combinatorial structure of meaningless elements arises the results of two pilot experiments are presented within which we observe human participants modifying a small lexicon of visual symbols through a process of iterated learning. As this lexicon evolves there is evidence that it becomes simpler and more learnable, more easily transmitted. I argue that these features are a consequence of spontaneous emergence of combinatorial, sub-lexical structure in the lexicon, that the pattern of emergence is more complex than the most widely espoused explanation suggests, and I propose ways in which future work can build on what we learn from these pilot experiments to confirm this hypothesis. PMID:23205149

  15. Tension control of space tether via online quasi-linearization iterations

    NASA Astrophysics Data System (ADS)

    Wen, Hao; Zhu, Zheng H.; Jin, Dongping; Hu, Haiyan

    2016-02-01

    The paper presents how to stabilize the deployment and retrieval processes of a space tether system via the tension control, where the model predictive control is exploited to optimize the control performance while the nonlinear dynamics and tension constraint are explicitly taken into account. A new scheme of online quasi-linearization iteration is proposed to transfer the nonlinear optimal control problem into a series of linear optimal control problems that can be solved in sequence at a series of sampling instants. Consequently, it avoids the complete solution of the nonlinear optimal control problem at each sampling interval such that the computational load can be greatly alleviated. Furthermore, the scheme extends the conventional quasi-linearization schemes by distributing the iterative process across sampling instants and online updating the initial condition of the linear optimal control problem. The problems of linear optimal control are discretized using a pseudo-spectral algorithm and then solved by a solver of linear quadratic programming. Numerical case studies indicate that successful deployment and retrieval of the system can be achieved using the proposed control scheme without violating the positive tension constraint. The time cost for each online optimization in the proposed scheme is on the order of 10 ms and far below the sampling interval under consideration.

  16. Decentralized Control of Sound Radiation from an Aircraft-Style Panel Using Iterative Loop Recovery

    NASA Technical Reports Server (NTRS)

    Schiller, Noah H.; Cabell, Randolph H.; Fuller, Chris R.

    2008-01-01

    A decentralized LQG-based control strategy is designed to reduce low-frequency sound transmission through periodically stiffened panels. While modern control strategies have been used to reduce sound radiation from relatively simple structural acoustic systems, significant implementation issues have to be addressed before these control strategies can be extended to large systems such as the fuselage of an aircraft. For instance, centralized approaches typically require a high level of connectivity and are computationally intensive, while decentralized strategies face stability problems caused by the unmodeled interaction between neighboring control units. Since accurate uncertainty bounds are not known a priori, it is difficult to ensure the decentralized control system will be robust without making the controller overly conservative. Therefore an iterative approach is suggested, which utilizes frequency-shaped loop recovery. The approach accounts for modeling error introduced by neighboring control loops, requires no communication between subsystems, and is relatively simple. The control strategy is validated using real-time control experiments performed on a built-up aluminum test structure representative of the fuselage of an aircraft. Experiments demonstrate that the iterative approach is capable of achieving 12 dB peak reductions and a 3.6 dB integrated reduction in radiated sound power from the stiffened panel.

  17. Wearable Sensor Data Classification for Human Activity Recognition Based on an Iterative Learning Framework.

    PubMed

    Davila, Juan Carlos; Cretu, Ana-Maria; Zaremba, Marek

    2017-06-07

    The design of multiple human activity recognition applications in areas such as healthcare, sports and safety relies on wearable sensor technologies. However, when making decisions based on the data acquired by such sensors in practical situations, several factors related to sensor data alignment, data losses, and noise, among other experimental constraints, deteriorate data quality and model accuracy. To tackle these issues, this paper presents a data-driven iterative learning framework to classify human locomotion activities such as walk, stand, lie, and sit, extracted from the Opportunity dataset. Data acquired by twelve 3-axial acceleration sensors and seven inertial measurement units are initially de-noised using a two-stage consecutive filtering approach combining a band-pass Finite Impulse Response (FIR) and a wavelet filter. A series of statistical parameters are extracted from the kinematical features, including the principal components and singular value decomposition of roll, pitch, yaw and the norm of the axial components. The novel interactive learning procedure is then applied in order to minimize the number of samples required to classify human locomotion activities. Only those samples that are most distant from the centroids of data clusters, according to a measure presented in the paper, are selected as candidates for the training dataset. The newly built dataset is then used to train an SVM multi-class classifier. The latter will produce the lowest prediction error. The proposed learning framework ensures a high level of robustness to variations in the quality of input data, while only using a much lower number of training samples and therefore a much shorter training time, which is an important consideration given the large size of the dataset.

  18. Current Control in ITER Steady State Plasmas With Neutral Beam Steering

    SciTech Connect

    R.V. Budny

    2009-09-10

    Predictions of quasi steady state DT plasmas in ITER are generated using the PTRANSP code. The plasma temperatures, densities, boundary shape, and total current (9 - 10 MA) anticipated for ITER steady state plasmas are specified. Current drive by negative ion neutral beam injection, lower-hybrid, and electron cyclotron resonance are calculated. Four modes of operation with different combinations of current drive are studied. For each mode, scans with the NNBI aimed at differing heights in the plasma are performed to study effects of current control on the q profile. The timeevolution of the currents and q are calculated to evaluate long duration transients. Quasi steady state, strongly reversed q profiles are predicted for some beam injection angles if the current drive and bootstrap currents are sufficiently large.

  19. Machine learning in motion control

    NASA Technical Reports Server (NTRS)

    Su, Renjeng; Kermiche, Noureddine

    1989-01-01

    The existing methodologies for robot programming originate primarily from robotic applications to manufacturing, where uncertainties of the robots and their task environment may be minimized by repeated off-line modeling and identification. In space application of robots, however, a higher degree of automation is required for robot programming because of the desire of minimizing the human intervention. We discuss a new paradigm of robotic programming which is based on the concept of machine learning. The goal is to let robots practice tasks by themselves and the operational data are used to automatically improve their motion performance. The underlying mathematical problem is to solve the problem of dynamical inverse by iterative methods. One of the key questions is how to ensure the convergence of the iterative process. There have been a few small steps taken into this important approach to robot programming. We give a representative result on the convergence problem.

  20. Machine learning in motion control

    NASA Technical Reports Server (NTRS)

    Su, Renjeng; Kermiche, Noureddine

    1989-01-01

    The existing methodologies for robot programming originate primarily from robotic applications to manufacturing, where uncertainties of the robots and their task environment may be minimized by repeated off-line modeling and identification. In space application of robots, however, a higher degree of automation is required for robot programming because of the desire of minimizing the human intervention. We discuss a new paradigm of robotic programming which is based on the concept of machine learning. The goal is to let robots practice tasks by themselves and the operational data are used to automatically improve their motion performance. The underlying mathematical problem is to solve the problem of dynamical inverse by iterative methods. One of the key questions is how to ensure the convergence of the iterative process. There have been a few small steps taken into this important approach to robot programming. We give a representative result on the convergence problem.

  1. Pole placement and order reduction in two-time-scale control systems through Riccati iteration

    NASA Technical Reports Server (NTRS)

    Anderson, L. R.

    1982-01-01

    A transformation of variables taken from singular perturbations may be applied to two-time-scale linear systems in state space form to reduce the system to block-diagonal form with slow and fast modes decoupled. The transformation is easily computed by applying the new Riccati iteration. The iteration yields a solution to the nonsymmetric algebraic Riccati equation obtained by partitioning the original system matrix A. The numerical procedure is initiated with the trivial iterate L(0) = 0, and is globally convergent to the desired unique time scale decoupling solution. After transformation, the decoupled system may be used in controller design to achieve exact closed loop pole placement in the slow subsystem without altering the poles of the fast subsystem. The decoupled form may also be used to reduce system order by wetting a small parameter to zero. Provided the fast subsystem is stable, the order reduction can be expected to yield a good approximation to the original system. These methods are demonstrated using the 16th order linear model of a turbofan engine.

  2. Iterative h-minima-based marker-controlled watershed for cell nucleus segmentation.

    PubMed

    Koyuncu, Can Fahrettin; Akhan, Ece; Ersahin, Tulin; Cetin-Atalay, Rengul; Gunduz-Demir, Cigdem

    2016-04-01

    Automated microscopy imaging systems facilitate high-throughput screening in molecular cellular biology research. The first step of these systems is cell nucleus segmentation, which has a great impact on the success of the overall system. The marker-controlled watershed is a technique commonly used by the previous studies for nucleus segmentation. These studies define their markers finding regional minima on the intensity/gradient and/or distance transform maps. They typically use the h-minima transform beforehand to suppress noise on these maps. The selection of the h value is critical; unnecessarily small values do not sufficiently suppress the noise, resulting in false and oversegmented markers, and unnecessarily large ones suppress too many pixels, causing missing and undersegmented markers. Because cell nuclei show different characteristics within an image, the same h value may not work to define correct markers for all the nuclei. To address this issue, in this work, we propose a new watershed algorithm that iteratively identifies its markers, considering a set of different h values. In each iteration, the proposed algorithm defines a set of candidates using a particular h value and selects the markers from those candidates provided that they fulfill the size requirement. Working with widefield fluorescence microscopy images, our experiments reveal that the use of multiple h values in our iterative algorithm leads to better segmentation results, compared to its counterparts. © 2016 International Society for Advancement of Cytometry. © 2016 International Society for Advancement of Cytometry.

  3. State-of-the-art Anonymization of Medical Records Using an Iterative Machine Learning Framework

    PubMed Central

    Szarvas, György; Farkas, Richárd; Busa-Fekete, Róbert

    2007-01-01

    Objective The anonymization of medical records is of great importance in the human life sciences because a de-identified text can be made publicly available for non-hospital researchers as well, to facilitate research on human diseases. Here the authors have developed a de-identification model that can successfully remove personal health information (PHI) from discharge records to make them conform to the guidelines of the Health Information Portability and Accountability Act. Design We introduce here a novel, machine learning-based iterative Named Entity Recognition approach intended for use on semi-structured documents like discharge records. Our method identifies PHI in several steps. First, it labels all entities whose tags can be inferred from the structure of the text and it then utilizes this information to find further PHI phrases in the flow text parts of the document. Measurements Following the standard evaluation method of the first Workshop on Challenges in Natural Language Processing for Clinical Data, we used token-level Precision, Recall and Fβ=1 measure metrics for evaluation. Results Our system achieved outstanding accuracy on the standard evaluation dataset of the de-identification challenge, with an F measure of 99.7534% for the best submitted model. Conclusion We can say that our system is competitive with the current state-of-the-art solutions, while we describe here several techniques that can be beneficial in other tasks that need to handle structured documents such as clinical records. PMID:17823086

  4. ROI reconstruction for model-based iterative reconstruction (MBIR) via a coupled dictionary learning

    NASA Astrophysics Data System (ADS)

    Ye, Dong Hye; Srivastava, Somesh; Thibault, Jean-Baptiste; Sauer, Ken D.; Bouman, Charles A.

    2017-03-01

    Model based iterative reconstruction (MBIR) algorithms have shown significant improvement in CT image quality by increasing resolution as well as reducing noise and artifacts. In diagnostic protocols, radiologists often need the high-resolution reconstruction of a limited region of interest (ROI). This ROI reconstruction is complicated for MBIR which should reconstruct an image in a full field of view (FOV) given full sinogram measurements. Multi-resolution approaches are widely used for this ROI reconstruction of MBIR, in which the image with a full FOV is reconstructed in a low-resolution and the forward projection of non-ROI is subtracted from the original sinogram measurements for high-resolution ROI reconstruction. However, a low-resolution reconstruction of a full FOV can be susceptible to streaking and blurring artifacts and these can be propagated into the following high-resolution ROI reconstruction. To tackle this challenge, we use a coupled dictionary representation model between low- and high-resolution training dataset for artifact removal and super resolution of a low-resolution full FOV reconstruction. Experimental results on phantom data show that the restored full FOV reconstruction via a coupled dictionary learning significantly improve the image quality of high-resolution ROI reconstruction for MBIR.

  5. Burn control of an ITER-like fusion reactor using fuzzy logic

    NASA Astrophysics Data System (ADS)

    Garcia-Amador, A. Sair; Martinell, Julio J.

    2016-10-01

    The fuel burn in a fusion reactor has to be kept at a nearly constant rate in order to have a steady power exhaust. Here, we develop a control system based on a fuzzy logic controller in order that adjusts external parameters to keep the plasma temperature and density at the design values of a reactor of the characteristics of ITER. The control parameters chosen are the D-T refueling rate, the auxiliary heating power and a neutral helium beam. We use a fuzzy controller of the Mamdani type that uses a number of membership functions appropriate to produce a response to parameter deviations that minimizes the response time. The inference rules are determined in a way to provide stabilization to all perturbations of the temperature, density and alpha particle fraction. The dynamical response of the reactor is simulated with a 0D model that uses confinement times provided by the ITER scaling. We show that the system is feedback stabilized for a large range of parameters around the nominal values. The recovery time after a departure from the steady values is of the order of one second. We compare the results with another control system based on neural networks that was developed previously. Funded by projects PAPIIT IN109115 and Conacyt 152905.

  6. A Tale of Two Chambers: Iterative Approaches and Lessons Learned from Life Support Systems Testing in Altitude Chambers

    NASA Technical Reports Server (NTRS)

    Callini, Gianluca

    2016-01-01

    The drive for the journey to Mars is in a higher gear than ever before. We are developing new spacecraft and life support systems to take humans to the Red Planet. The journey that development hardware takes before its final incarnation in a fully integrated spacecraft can take years, as is the case for the Orion environmental control and life support system (ECLSS). Through the Pressure Integrated Suit Test (PIST) series, NASA personnel at Johnson Space Center have been characterizing the behavior of a closed loop ECLSS in the event of cabin depressurization. This kind of testing - one of the most hazardous activities performed at JSC - requires an iterative approach, increasing in complexity and hazards). The PIST series, conducted in the Crew and Thermal Systems Division (CTSD) 11-ft Chamber, started with unmanned test precursors before moving to a human-in-the-loop phase, and continues to evolve with the eventual goal of a qualification test for the final system that will be installed on Orion. Meanwhile, the Human Exploration Spacecraft Testbed for Integration and Advancement (HESTIA) program is an effort to research and develop technologies that will work in concert to support habitation on Mars. September 2015 marked the first unmanned HESTIA test, with the goal of characterizing how ECLSS technologies work together in a closed environment. HESTIA will culminate in crewed testing, but it can benefit from the lessons learned from another test that is farther ahead in its development and life cycle. Discussing PIST and HESTIA, this paper illustrates how we approach testing, the kind of information that facility teams need to ensure efficient collaborations and successful testing, and how we can apply what we learn to execute future tests.

  7. Performance analysis of model based iterative reconstruction with dictionary learning in transportation security CT

    NASA Astrophysics Data System (ADS)

    Haneda, Eri; Luo, Jiajia; Can, Ali; Ramani, Sathish; Fu, Lin; De Man, Bruno

    2016-05-01

    In this study, we implement and compare model based iterative reconstruction (MBIR) with dictionary learning (DL) over MBIR with pairwise pixel-difference regularization, in the context of transportation security. DL is a technique of sparse signal representation using an over complete dictionary which has provided promising results in image processing applications including denoising,1 as well as medical CT reconstruction.2 It has been previously reported that DL produces promising results in terms of noise reduction and preservation of structural details, especially for low dose and few-view CT acquisitions.2 A distinguishing feature of transportation security CT is that scanned baggage may contain items with a wide range of material densities. While medical CT typically scans soft tissues, blood with and without contrast agents, and bones, luggage typically contains more high density materials (i.e. metals and glass), which can produce severe distortions such as metal streaking artifacts. Important factors of security CT are the emphasis on image quality such as resolution, contrast, noise level, and CT number accuracy for target detection. While MBIR has shown exemplary performance in the trade-off of noise reduction and resolution preservation, we demonstrate that DL may further improve this trade-off. In this study, we used the KSVD-based DL3 combined with the MBIR cost-minimization framework and compared results to Filtered Back Projection (FBP) and MBIR with pairwise pixel-difference regularization. We performed a parameter analysis to show the image quality impact of each parameter. We also investigated few-view CT acquisitions where DL can show an additional advantage relative to pairwise pixel difference regularization.

  8. Dual-dictionary learning-based iterative image reconstruction for spectral computed tomography application

    NASA Astrophysics Data System (ADS)

    Zhao, Bo; Ding, Huanjun; Lu, Yang; Wang, Ge; Zhao, Jun; Molloi, Sabee

    2012-12-01

    In this study, we investigated the effectiveness of a novel iterative reconstruction (IR) method coupled with dual-dictionary learning (DDL) for image reconstruction in a dedicated breast computed tomography (CT) system based on a cadmium-zinc-telluride (CZT) photon-counting detector and compared it to the filtered-back-projection (FBP) method with the ultimate goal of reducing the number of projections necessary for reconstruction without sacrificing the image quality. Postmortem breast samples were scanned in a fan-beam CT system and were reconstructed from 100 to 600 projections with both IR and FBP methods. The contrast-to-noise ratio (CNR) between the glandular and adipose tissues of the postmortem breast samples was calculated to compare the quality of images reconstructed from IR and FBP. The spatial resolution of the two reconstruction techniques was evaluated using aluminum (Al) wires with diameters of 643, 813, 1020, 1290 and 1630 µm in a plastic epoxy resin phantom with a diameter of 13 cm. Both the spatial resolution and the CNR were improved with IR compared to FBP for the images reconstructed from the same number of projections. In comparison with FBP reconstruction, the CNR was improved from 3.4 to 7.5 by using the IR method with six-fold fewer projections while maintaining the same spatial resolution. The study demonstrated that the IR method coupled with DDL could significantly reduce the required number of projections for a CT reconstruction compared to the FBP method while achieving a much better CNR and maintaining the same spatial resolution. From this, the radiation dose and scanning time can potentially be reduced by a factor of approximately 6 by using this IR method for image reconstruction in a CZT-based breast CT system.

  9. Dual-Dictionary Learning-Based Iterative Image Reconstruction for Spectral Computed Tomography Application

    PubMed Central

    Zhao, Bo; Ding, Huanjun; Lu, Yang; Wang, Ge; Zhao, Jun; Molloi, Sabee

    2015-01-01

    In this study, we investigated the effectiveness of a novel Iterative Reconstruction (IR) method coupled with Dual-Dictionary Learning (DDL) for image reconstruction in a dedicated breast Computed Tomography (CT) system based on a Cadmium-Zinc-Telluride (CZT) photon-counting detector and compared it to the Filtered-Back-Projection (FBP) method with the ultimate goal of reducing the number of projections necessary for reconstruction without sacrificing image quality. Postmortem breast samples were scanned in a fan-beam CT system and were reconstructed from 100–600 projections with both IR and FBP methods. The Contrast-to-Noise Ratio (CNR) between the glandular and adipose tissues of the postmortem breast samples was calculated to compare the quality of images reconstructed from IR and FBP. The spatial resolution of the two reconstruction techniques was evaluated using Aluminum (Al) wires with diameters of 643, 813, 1020, 1290 and 1630 µm in a plastic epoxy resin phantom with diameter of 13 cm. Both the spatial resolution and the CNR were improved with IR compared to FBP for the images reconstructed from the same number of projections. In comparison with FBP reconstruction, the CNR was improved from 3.4 to 7.5 by using the IR method with 6-fold fewer projections while maintaining the same spatial resolution. The study demonstrated that the IR method coupled with DDL could significantly reduce the required number of projections for a CT reconstruction compared to FBP method while achieving a much better CNR and maintaining the same spatial resolution. From this, the radiation dose and scanning time can potentially be reduced by a factor of approximately 6 by using this IR method for image reconstruction in a CZT-based breast CT system. PMID:23192234

  10. eNOSHA, a Free, Open and Flexible Learning Object Repository--An Iterative Development Process for Global User-Friendliness

    ERIC Educational Resources Information Center

    Mozelius, Peter; Hettiarachchi, Enosha

    2012-01-01

    This paper describes the iterative development process of a Learning Object Repository (LOR), named eNOSHA. Discussions on a project for a LOR started at the e-Learning Centre (eLC) at The University of Colombo, School of Computing (UCSC) in 2007. The eLC has during the last decade been developing learning content for a nationwide e-learning…

  11. Real-time sawtooth control and neoclassical tearing mode preemption in ITER

    SciTech Connect

    Kim, D. Goodman, T. P.; Sauter, O.

    2014-06-15

    Real-time control of multiple plasma actuators is a requirement in advanced tokamaks; for example, for burn control, plasma current profile control and MHD stabilization—electron cyclotron (EC) wave absorption is ideally suited especially for the latter. On ITER, 24 EC sources can be switched between 56 inputs at the torus. In the torus, 5 launchers direct the power to various locations across the plasma profile via 11 steerable mirrors. For optimal usage of the available power, the aiming and polarization of the beams must be adapted to the plasma configuration and the needs of the scenario. Since the EC system performs many competing tasks, present day systems should demonstrate the ability of an EC plant to deal with several targets in parallel and/or to switch smoothly between goals to attain overall satisfaction. Based on pacing and locking experiments performed on TCV (Tokamak à Configuration Variable), the real-time sawtooth control of ITER with this complex set of actuators is analyzed, as an example. It is shown that sawtooth locking and pacing are possible with various levels of powers, leading to different time delays between the end of the EC power phase and the next sawtooth crash. This timing is important since it allows use of the same launchers for neoclassical tearing mode (NTM) preemption at the q = 1.5 or 2 surface, avoiding the need to switch power between launchers. These options are presented. It is also demonstrated that increasing the total EC power does not necessarily increase the range of control because of the geometry of the launchers.

  12. 17th Workshop on MHD Stability Control: addressing the disruption challenge for ITER

    NASA Astrophysics Data System (ADS)

    Buttery, Richard

    2013-08-01

    This annual workshop on magnetohydrodynamic stability control was held on 5-7 November 2012 at Columbia University in the city of New York, in the aftermath of a violent hydrodynamic instability event termed 'Hurricane Sandy'. Despite these challenging circumstances, Columbia University managed an excellent meeting, enabling the full participation of the community. This Workshop has been held since 1996 to help in the development of understanding and control of magnetohydrodynamic (MHD) instabilities for future fusion reactors. It covers a wide range of stability topics—from disruptions, to tearing modes, error fields, edge-localized modes (ELMs), resistive wall modes (RWMs) and ideal MHD—spanning many device types (tokamaks, stellarators and reversed field pinches) to identify commonalities in the physics and a means of control. The theme for 2012 was 'addressing the disruption challenge for ITER', and thus the first day had a heavy focus on both the avoidance and mitigation of disruptions in ITER. Key elements included understanding how to apply 3D fields to maintain stability, as well as managing the disruption process itself through mitigating loads in the thermal quench and handling so called 'runaway electrons'. This culminated in a panel discussion on the disruption mitigation strategy for ITER, which noted that heat load asymmetries during the thermal quench appear to be an artifact of MHD processes, and that runaway electron generation may be inevitable, suggesting research should focus on control and dissipation of the runaway beam. The workshop was combined this year with the annual US-Japan MHD Workshop, with a special section looking more deeply at 'Fundamentals of 3D Perturbed Equilibrium Control', with interesting sessions on 3D equilibrium reconstruction, RWM physics, novel control concepts such as non-magnetic sensing, adaptive control, q < 2 tokamak operation, and the effects of flow. The final day turned to tearing mode interactions

  13. Numerical analysis of a reinforcement learning model with the dynamic aspiration level in the iterated Prisoner's dilemma.

    PubMed

    Masuda, Naoki; Nakamura, Mitsuhiro

    2011-06-07

    Humans and other animals can adapt their social behavior in response to environmental cues including the feedback obtained through experience. Nevertheless, the effects of the experience-based learning of players in evolution and maintenance of cooperation in social dilemma games remain relatively unclear. Some previous literature showed that mutual cooperation of learning players is difficult or requires a sophisticated learning model. In the context of the iterated Prisoner's dilemma, we numerically examine the performance of a reinforcement learning model. Our model modifies those of Karandikar et al. (1998), Posch et al. (1999), and Macy and Flache (2002) in which players satisfy if the obtained payoff is larger than a dynamic threshold. We show that players obeying the modified learning mutually cooperate with high probability if the dynamics of threshold is not too fast and the association between the reinforcement signal and the action in the next round is sufficiently strong. The learning players also perform efficiently against the reactive strategy. In evolutionary dynamics, they can invade a population of players adopting simpler but competitive strategies. Our version of the reinforcement learning model does not complicate the previous model and is sufficiently simple yet flexible. It may serve to explore the relationships between learning and evolution in social dilemma situations.

  14. An Information-Based Learning Approach to Dual Control.

    PubMed

    Alpcan, Tansu; Shames, Iman

    2015-11-01

    Dual control aims to concurrently learn and control an unknown system. However, actively learning the system conflicts directly with any given control objective for it will disturb the system during exploration. This paper presents a receding horizon approach to dual control, where a multiobjective optimization problem is solved repeatedly and subject to constraints representing system dynamics. Balancing a standard finite-horizon control objective, a knowledge gain objective is defined to explicitly quantify the information acquired when learning the system dynamics. Measures from information theory, such as entropy-based uncertainty, Fisher information, and relative entropy, are studied and used to quantify the knowledge gained as a result of the control actions. The resulting iterative framework is applied to Markov decision processes and discrete-time nonlinear systems. Thus, the broad applicability and usefulness of the presented approach is demonstrated in diverse problem settings. The framework is illustrated with multiple numerical examples.

  15. Examination of the Entry to Burn and Burn Control for the ITER 15 MA Baseline and Other Scenarios

    SciTech Connect

    Kesse, Charles E.; Kim, S-H.; Koechl, F.

    2014-09-01

    The entry to burn and flattop burn control in ITER will be a critical need from the first DT experiments. Simulations are used to address time-dependent behavior under a range of possible conditions that include injected power level, impurity content (W, Ar, Be), density evolution, H-mode regimes, controlled parameter (Wth, Pnet, Pfusion), and actuator (Paux, fueling, fAr), with a range of transport models. A number of physics issues at the L-H transition require better understanding to project to ITER, however, simulations indicate viable control with sufficient auxiliary power (up to 73 MW), while lower powers become marginal (as low as 43 MW).

  16. Procedural Learning during Declarative Control

    ERIC Educational Resources Information Center

    Crossley, Matthew J.; Ashby, F. Gregory

    2015-01-01

    There is now abundant evidence that human learning and memory are governed by multiple systems. As a result, research is now turning to the next question of how these putative systems interact. For instance, how is overall control of behavior coordinated, and does learning occur independently within systems regardless of what system is in control?…

  17. Procedural Learning during Declarative Control

    ERIC Educational Resources Information Center

    Crossley, Matthew J.; Ashby, F. Gregory

    2015-01-01

    There is now abundant evidence that human learning and memory are governed by multiple systems. As a result, research is now turning to the next question of how these putative systems interact. For instance, how is overall control of behavior coordinated, and does learning occur independently within systems regardless of what system is in control?…

  18. Learning fuzzy logic control system

    NASA Technical Reports Server (NTRS)

    Lung, Leung Kam

    1994-01-01

    The performance of the Learning Fuzzy Logic Control System (LFLCS), developed in this thesis, has been evaluated. The Learning Fuzzy Logic Controller (LFLC) learns to control the motor by learning the set of teaching values that are generated by a classical PI controller. It is assumed that the classical PI controller is tuned to minimize the error of a position control system of the D.C. motor. The Learning Fuzzy Logic Controller developed in this thesis is a multi-input single-output network. Training of the Learning Fuzzy Logic Controller is implemented off-line. Upon completion of the training process (using Supervised Learning, and Unsupervised Learning), the LFLC replaces the classical PI controller. In this thesis, a closed loop position control system of a D.C. motor using the LFLC is implemented. The primary focus is on the learning capabilities of the Learning Fuzzy Logic Controller. The learning includes symbolic representation of the Input Linguistic Nodes set and Output Linguistic Notes set. In addition, we investigate the knowledge-based representation for the network. As part of the design process, we implement a digital computer simulation of the LFLCS. The computer simulation program is written in 'C' computer language, and it is implemented in DOS platform. The LFLCS, designed in this thesis, has been developed on a IBM compatible 486-DX2 66 computer. First, the performance of the Learning Fuzzy Logic Controller is evaluated by comparing the angular shaft position of the D.C. motor controlled by a conventional PI controller and that controlled by the LFLC. Second, the symbolic representation of the LFLC and the knowledge-based representation for the network are investigated by observing the parameters of the Fuzzy Logic membership functions and the links at each layer of the LFLC. While there are some limitations of application with this approach, the result of the simulation shows that the LFLC is able to control the angular shaft position of the

  19. On the Sequential Control of ITER Poloidal Field Converters for Reactive Power Reduction

    NASA Astrophysics Data System (ADS)

    Yuan, Hongwen; Fu, Peng; Gao, Ge; Huang, Liansheng; Song, Zhiquan; He, Shiying; Wu, Yanan; Dong, Lin; Wang, Min; Fang, Tongzhen

    2014-12-01

    Sequential control applied to the International Thermonuclear Experimental Reactor (ITER) poloidal field converter system for the purpose of reactive power reduction is the subject of this investigation. Due to the inherent characteristics of thyristor-based phase-controlled converter, the poloidal field converter system consumes a huge amount of reactive power from the grid, which subsequently results in a voltage drop at the 66 kV busbar if no measure is taken. The installation of a static var compensator rated for 750 MVar at the 66 kV busbar is an essential way to compensate reactive power to the grid, which is the most effective measure to solve the problem. However, sequential control of the multi-series converters provides an additional method to improve the natural power factor and thus alleviate the pressure of reactive power demand of the converter system without any additional cost. In the present paper, by comparing with the symmetrical control technique, the advantage of sequential control in reactive power consumption is highlighted. Simulation results based on SIMULINK are found in agreement with the theoretical analysis.

  20. Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning

    PubMed Central

    Heffernan, Rhys; Paliwal, Kuldip; Lyons, James; Dehzangi, Abdollah; Sharma, Alok; Wang, Jihua; Sattar, Abdul; Yang, Yuedong; Zhou, Yaoqi

    2015-01-01

    Direct prediction of protein structure from sequence is a challenging problem. An effective approach is to break it up into independent sub-problems. These sub-problems such as prediction of protein secondary structure can then be solved independently. In a previous study, we found that an iterative use of predicted secondary structure and backbone torsion angles can further improve secondary structure and torsion angle prediction. In this study, we expand the iterative features to include solvent accessible surface area and backbone angles and dihedrals based on Cα atoms. By using a deep learning neural network in three iterations, we achieved 82% accuracy for secondary structure prediction, 0.76 for the correlation coefficient between predicted and actual solvent accessible surface area, 19° and 30° for mean absolute errors of backbone φ and ψ angles, respectively, and 8° and 32° for mean absolute errors of Cα-based θ and τ angles, respectively, for an independent test dataset of 1199 proteins. The accuracy of the method is slightly lower for 72 CASP 11 targets but much higher than those of model structures from current state-of-the-art techniques. This suggests the potentially beneficial use of these predicted properties for model assessment and ranking. PMID:26098304

  1. Learning to Teach Elementary Science through Iterative Cycles of Enactment in Culturally and Linguistically Diverse Contexts

    ERIC Educational Resources Information Center

    Bottoms, SueAnn I.; Ciechanowski, Kathryn M.; Hartman, Brian

    2015-01-01

    Iterative cycles of enactment embedded in culturally and linguistically diverse contexts provide rich opportunities for preservice teachers (PSTs) to enact core practices of science. This study is situated in the larger Families Involved in Sociocultural Teaching and Science, Technology, Engineering and Mathematics (FIESTAS) project, which weaves…

  2. Learning to Teach Elementary Science through Iterative Cycles of Enactment in Culturally and Linguistically Diverse Contexts

    ERIC Educational Resources Information Center

    Bottoms, SueAnn I.; Ciechanowski, Kathryn M.; Hartman, Brian

    2015-01-01

    Iterative cycles of enactment embedded in culturally and linguistically diverse contexts provide rich opportunities for preservice teachers (PSTs) to enact core practices of science. This study is situated in the larger Families Involved in Sociocultural Teaching and Science, Technology, Engineering and Mathematics (FIESTAS) project, which weaves…

  3. Hybrid learning control for improving suppression of hand tremor.

    PubMed

    As'arry, Azizan; Md Zain, Mohd Zarhamdy; Mailah, Musa; Hussein, Mohamed

    2013-11-01

    Patients with hand tremors may find routine activities such as writing and holding objects affected. In response to this problem, an active control technique has been examined in order to lessen the severity of tremors. In this article, an online method of a hybrid proportional-integral control with active force control strategy for tremor attenuation is presented. An intelligent mechanism using iterative learning control is incorporated into the active force control loop to approximate the estimation mass parameter. Experiments were conducted on a dummy hand model placed horizontally in a tremor test rig. When activated by a shaker in the vertical direction, this resembles a postural tremor condition. In the proportional-integral plus active force control, a linear voice coil actuator is used as the main active tremor suppressive element. A sensitivity analysis is presented to investigate the robustness of the proposed controller in a real-time control environment. The findings of this study demonstrate that the intelligent active force control and iterative learning controller show excellent performance in reducing tremor error compared to classic pure proportional, proportional-integral and hybrid proportional-integral plus active force control controllers.

  4. Procedural learning during declarative control.

    PubMed

    Crossley, Matthew J; Ashby, F Gregory

    2015-09-01

    There is now abundant evidence that human learning and memory are governed by multiple systems. As a result, research is now turning to the next question of how these putative systems interact. For instance, how is overall control of behavior coordinated, and does learning occur independently within systems regardless of what system is in control? Behavioral, neuroimaging, and neuroscience data are somewhat mixed with respect to these questions. Human neuroimaging and animal lesion studies suggest independent learning and are mostly agnostic with respect to control. Human behavioral studies suggest active inhibition of behavioral output but have little to say regarding learning. The results of two perceptual category-learning experiments are described that strongly suggest that procedural learning does occur while the explicit system is in control of behavior and that this learning might be just as good as if the procedural system was controlling the response. These results are consistent with the idea that declarative memory systems inhibit the ability of the procedural system to access motor output systems but do not prevent procedural learning.

  5. Robust Control Feedback and Learning

    DTIC Science & Technology

    2002-11-30

    98-1-0026 5b. GRANT NUMBER Robust Control, Feedback and Learning F49620-98-1-0026 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER Michael G...Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std. Z39.18 Final Report: ROBUST CONTROL FEEDBACK AND LEARNING AFOSR Grant F49620-98-1-0026 October 1...Philadelphia, PA, 2000. [16] M. G. Safonov. Recent advances in robust control, feedback and learning . In S. 0. R. Moheimani, editor, Perspectives in Robust

  6. Progress on the application of ELM control schemes to ITER scenarios from the non-active phase to DT operation

    NASA Astrophysics Data System (ADS)

    Loarte, A.; Huijsmans, G.; Futatani, S.; Baylor, L. R.; Evans, T. E.; Orlov, D. M.; Schmitz, O.; Becoulet, M.; Cahyna, P.; Gribov, Y.; Kavin, A.; Sashala Naik, A.; Campbell, D. J.; Casper, T.; Daly, E.; Frerichs, H.; Kischner, A.; Laengner, R.; Lisgo, S.; Pitts, R. A.; Saibene, G.; Wingen, A.

    2014-03-01

    Progress in the definition of the requirements for edge localized mode (ELM) control and the application of ELM control methods both for high fusion performance DT operation and non-active low-current operation in ITER is described. Evaluation of the power fluxes for low plasma current H-modes in ITER shows that uncontrolled ELMs will not lead to damage to the tungsten (W) divertor target, unlike for high-current H-modes in which divertor damage by uncontrolled ELMs is expected. Despite the lack of divertor damage at lower currents, ELM control is found to be required in ITER under these conditions to prevent an excessive contamination of the plasma by W, which could eventually lead to an increased disruptivity. Modelling with the non-linear MHD code JOREK of the physics processes determining the flow of energy from the confined plasma onto the plasma-facing components during ELMs at the ITER scale shows that the relative contribution of conductive and convective losses is intrinsically linked to the magnitude of the ELM energy loss. Modelling of the triggering of ELMs by pellet injection for DIII-D and ITER has identified the minimum pellet size required to trigger ELMs and, from this, the required fuel throughput for the application of this technique to ITER is evaluated and shown to be compatible with the installed fuelling and tritium re-processing capabilities in ITER. The evaluation of the capabilities of the ELM control coil system in ITER for ELM suppression is carried out (in the vacuum approximation) and found to have a factor of ˜2 margin in terms of coil current to achieve its design criterion, although such a margin could be substantially reduced when plasma shielding effects are taken into account. The consequences for the spatial distribution of the power fluxes at the divertor of ELM control by three-dimensional (3D) fields are evaluated and found to lead to substantial toroidal asymmetries in zones of the divertor target away from the separatrix

  7. SVM-Hustle - An iterative semi-supervised machine learning approach for pairwise protein remote homology detection

    SciTech Connect

    Shah, Anuj R.; Oehmen, Chris S.; Webb-Robertson, Bobbie-Jo M.

    2008-03-15

    Motivation: As the amount of biological sequence data continues to grow exponentially we face the increasing challenge of assigning function to this enormous molecular ‘parts list’. The most popular approaches to this challenge make use of the simplifying assumption that similar functional molecules, or proteins, sometimes have similar composition, or sequence. However, these algorithms often fail to identify remote homologs (proteins with similar function but dissimilar sequence) which often are a significant fraction of the total homolog collection for a given sequence. We introduce a Support Vector Machine (SVM)-based tool to detect Homology Using Semisupervised iTerative LEarning (SVM-HUSTLE) that detects significantly more remote homologs than current state-of-the-art sequence or cluster-based methods. As opposed to building profiles or position specific scoring matrices, SVM-HUSTLE builds an SVM classifier for a query sequence by training on a collection of representative highconfidence training sets. SVM-HUSTLE combines principles of semi-supervised learning theory with statistical sampling to create many concurrent classifiers to iteratively detect and refine on-the-fly patterns indicating homology. Results: When compared against existing methods for identifying protein homologs (BLASTp, PSI-BLAST, RANKPROP, MOTIFPROP and their variants) on the SCOP 1.59 benchmark dataset consisting of 7329 protein sequences, SVM-HUSTLE significantly outperforms each of the above methods using the most stringent ROC1 statistic with p-values less than 1e-20.

  8. Aircraft adaptive learning control

    NASA Technical Reports Server (NTRS)

    Lee, P. S. T.; Vanlandingham, H. F.

    1979-01-01

    The optimal control theory of stochastic linear systems is discussed in terms of the advantages of distributed-control systems, and the control of randomly-sampled systems. An optimal solution to longitudinal control is derived and applied to the F-8 DFBW aircraft. A randomly-sampled linear process model with additive process and noise is developed.

  9. Reinforcement learning for robot control

    NASA Astrophysics Data System (ADS)

    Smart, William D.; Pack Kaelbling, Leslie

    2002-02-01

    Writing control code for mobile robots can be a very time-consuming process. Even for apparently simple tasks, it is often difficult to specify in detail how the robot should accomplish them. Robot control code is typically full of magic numbers that must be painstakingly set for each environment that the robot must operate in. The idea of having a robot learn how to accomplish a task, rather than being told explicitly is an appealing one. It seems easier and much more intuitive for the programmer to specify what the robot should be doing, and let it learn the fine details of how to do it. In this paper, we describe JAQL, a framework for efficient learning on mobile robots, and present the results of using it to learn control policies for simple tasks.

  10. Fast and automatic depth control of iterative bone ablation based on optical coherence tomography data

    NASA Astrophysics Data System (ADS)

    Fuchs, Alexander; Pengel, Steffen; Bergmeier, Jan; Kahrs, Lüder A.; Ortmaier, Tobias

    2015-07-01

    Laser surgery is an established clinical procedure in dental applications, soft tissue ablation, and ophthalmology. The presented experimental set-up for closed-loop control of laser bone ablation addresses a feedback system and enables safe ablation towards anatomical structures that usually would have high risk of damage. This study is based on combined working volumes of optical coherence tomography (OCT) and Er:YAG cutting laser. High level of automation in fast image data processing and tissue treatment enables reproducible results and shortens the time in the operating room. For registration of the two coordinate systems a cross-like incision is ablated with the Er:YAG laser and segmented with OCT in three distances. The resulting Er:YAG coordinate system is reconstructed. A parameter list defines multiple sets of laser parameters including discrete and specific ablation rates as ablation model. The control algorithm uses this model to plan corrective laser paths for each set of laser parameters and dynamically adapts the distance of the laser focus. With this iterative control cycle consisting of image processing, path planning, ablation, and moistening of tissue the target geometry and desired depth are approximated until no further corrective laser paths can be set. The achieved depth stays within the tolerances of the parameter set with the smallest ablation rate. Specimen trials with fresh porcine bone have been conducted to prove the functionality of the developed concept. Flat bottom surfaces and sharp edges of the outline without visual signs of thermal damage verify the feasibility of automated, OCT controlled laser bone ablation with minimal process time.

  11. An Optimization of Maximal Invariance in a Class of Multiple Valued Iterative Dynamics Models of Nonlinear Disturbed Control Systems

    NASA Astrophysics Data System (ADS)

    Kahng, Byungik

    2016-11-01

    We discuss an optimization problem on the maximal invariance of a class of multiple-valued iterative dynamics (MVID) models of discrete-time nonlinear control dynamical systems with singular disturbance. We study the inner and outer bounds of the maximal invariance, between which all noninterfering MVID models of nonlinear discrete-time control dynamical systems with singular disturbance reside. We also study the invariant fractal structure and an optimization of Lyapunov multipliers associated to it.

  12. Circuit model of the ITER-like antenna for JET and simulation of its control algorithms

    NASA Astrophysics Data System (ADS)

    Durodié, Frédéric; Dumortier, Pierre; Helou, Walid; Křivská, Alena; Lerche, Ernesto

    2015-12-01

    The ITER-like Antenna (ILA) for JET [1] is a 2 toroidal by 2 poloidal array of Resonant Double Loops (RDL) featuring in-vessel matching capacitors feeding RF current straps in conjugate-T manner, a low impedance quarter-wave impedance transformer, a service stub allowing hydraulic actuator and water cooling services to reach the aforementioned capacitors and a 2nd stage phase-shifter-stub matching circuit allowing to correct/choose the conjugate-T working impedance. Toroidally adjacent RDLs are fed from a 3dB hybrid splitter. It has been operated at 33, 42 and 47MHz on plasma (2008-2009) while it presently estimated frequency range is from 29 to 49MHz. At the time of the design (2001-2004) as well as the experiments the circuit models of the ILA were quite basic. The ILA front face and strap array Topica model was relatively crude and failed to correctly represent the poloidal central septum, Faraday Screen attachment as well as the segmented antenna central septum limiter. The ILA matching capacitors, T-junction, Vacuum Transmission Line (VTL) and Service Stubs were represented by lumped circuit elements and simple transmission line models. The assessment of the ILA results carried out to decide on the repair of the ILA identified that achieving routine full array operation requires a better understanding of the RF circuit, a feedback control algorithm for the 2nd stage matching as well as tighter calibrations of RF measurements. The paper presents the progress in modelling of the ILA comprising a more detailed Topica model of the front face for various plasma Scrape Off Layer profiles, a comprehensive HFSS model of the matching capacitors including internal bellows and electrode cylinders, 3D-EM models of the VTL including vacuum ceramic window, Service stub, a transmission line model of the 2nd stage matching circuit and main transmission lines including the 3dB hybrid splitters. A time evolving simulation using the improved circuit model allowed to design and

  13. Circuit model of the ITER-like antenna for JET and simulation of its control algorithms

    SciTech Connect

    Durodié, Frédéric Křivská, Alena; Helou, Walid; Collaboration: EUROfusion Consortium

    2015-12-10

    The ITER-like Antenna (ILA) for JET [1] is a 2 toroidal by 2 poloidal array of Resonant Double Loops (RDL) featuring in-vessel matching capacitors feeding RF current straps in conjugate-T manner, a low impedance quarter-wave impedance transformer, a service stub allowing hydraulic actuator and water cooling services to reach the aforementioned capacitors and a 2nd stage phase-shifter-stub matching circuit allowing to correct/choose the conjugate-T working impedance. Toroidally adjacent RDLs are fed from a 3dB hybrid splitter. It has been operated at 33, 42 and 47MHz on plasma (2008-2009) while it presently estimated frequency range is from 29 to 49MHz. At the time of the design (2001-2004) as well as the experiments the circuit models of the ILA were quite basic. The ILA front face and strap array Topica model was relatively crude and failed to correctly represent the poloidal central septum, Faraday Screen attachment as well as the segmented antenna central septum limiter. The ILA matching capacitors, T-junction, Vacuum Transmission Line (VTL) and Service Stubs were represented by lumped circuit elements and simple transmission line models. The assessment of the ILA results carried out to decide on the repair of the ILA identified that achieving routine full array operation requires a better understanding of the RF circuit, a feedback control algorithm for the 2nd stage matching as well as tighter calibrations of RF measurements. The paper presents the progress in modelling of the ILA comprising a more detailed Topica model of the front face for various plasma Scrape Off Layer profiles, a comprehensive HFSS model of the matching capacitors including internal bellows and electrode cylinders, 3D-EM models of the VTL including vacuum ceramic window, Service stub, a transmission line model of the 2nd stage matching circuit and main transmission lines including the 3dB hybrid splitters. A time evolving simulation using the improved circuit model allowed to design and

  14. Reflections on the Use of Iterative, Agile and Collaborative Approaches for Blended Flipped Learning Development

    ERIC Educational Resources Information Center

    Owen, Hazel; Dunham, Nicola

    2015-01-01

    E-learning experiences are widely becoming common practice in many schools, tertiary institutions and other organisations. However despite this increased use of technology to enhance learning and the associated investment involved the result does not always equate to more engaged, knowledgeable and skilled learners. We have observed two key…

  15. Newton iteration acceleration of the Nash game algorithm for power control in 3G wireless CDMA networks

    NASA Astrophysics Data System (ADS)

    Gajic, Zoran R.; Koskie, Sarah

    2003-08-01

    In wireless communication systems, each user's signal contributes to the interference seen by the other users. Given limited available battery power, this creates a need for effective and efficient power control strategies. These strategies may be designed to achieve quality of service (QoS) or system capacity objectives, or both. We show how the power control problem is naturally suited to formulation as a noncooperative game in which users choose to trade off between signal-to-interference ratio (SIR) error and power usage. Koskie (2003) studied the static Nash game formulation of this problem. The solution obtained led to a system of nonlinear algebraic equations. In this paper we present a novel distributed power control strategy based on the Newton iteration used to solve the corresponding algebraic equations. That method accelerates the convergence of the Nash game algorithm owing to the quadratic convergence of the Newton iterations. A numerical example demonstrates the efficiency of the new algorithm.

  16. Performance improvement of robots using a learning control scheme

    NASA Technical Reports Server (NTRS)

    Krishna, Ramuhalli; Chiang, Pen-Tai; Yang, Jackson C. S.

    1987-01-01

    Many applications of robots require that the same task be repeated a number of times. In such applications, the errors associated with one cycle are also repeated every cycle of the operation. An off-line learning control scheme is used here to modify the command function which would result in smaller errors in the next operation. The learning scheme is based on a knowledge of the errors and error rates associated with each cycle. Necessary conditions for the iterative scheme to converge to zero errors are derived analytically considering a second order servosystem model. Computer simulations show that the errors are reduced at a faster rate if the error rate is included in the iteration scheme. The results also indicate that the scheme may increase the magnitude of errors if the rate information is not included in the iteration scheme. Modification of the command input using a phase and gain adjustment is also proposed to reduce the errors with one attempt. The scheme is then applied to a computer model of a robot system similar to PUMA 560. Improved performance of the robot is shown by considering various cases of trajectory tracing. The scheme can be successfully used to improve the performance of actual robots within the limitations of the repeatability and noise characteristics of the robot.

  17. Metacognitive control and optimal learning.

    PubMed

    Son, Lisa K; Sethi, Rajiv

    2006-07-08

    The notion of optimality is often invoked informally in the literature on metacognitive control. We provide a precise formulation of the optimization problem and show that optimal time allocation strategies depend critically on certain characteristics of the learning environment, such as the extent of time pressure, and the nature of the uptake function. When the learning curve is concave, optimality requires that items at lower levels of initial competence be allocated greater time. On the other hand, with logistic learning curves, optimal allocations vary with time availability in complex and surprising ways. Hence there are conditions under which optimal strategies will be relatively easy to uncover, and others in which suboptimal time allocation might be expected. The model can therefore be used to address the question of whether and when learners should be able to exercise good metacognitive control in practice.

  18. Learner-Controlled Scaffolding Linked to Open-Ended Problems in a Digital Learning Environment

    ERIC Educational Resources Information Center

    Edson, Alden Jack

    2017-01-01

    This exploratory study reports on how students activated learner-controlled scaffolding and navigated through sequences of connected problems in a digital learning environment. A design experiment was completed to (re)design, iteratively develop, test, and evaluate a digital version of an instructional unit focusing on binomial distributions and…

  19. Online Supplementary ADP Learning Controller Design and Application to Power System Frequency Control With Large-Scale Wind Energy Integration.

    PubMed

    Guo, Wentao; Liu, Feng; Si, Jennie; He, Dawei; Harley, Ronald; Mei, Shengwei

    2016-08-01

    The emergence of smart grids has posed great challenges to traditional power system control given the multitude of new risk factors. This paper proposes an online supplementary learning controller (OSLC) design method to compensate the traditional power system controllers for coping with the dynamic power grid. The proposed OSLC is a supplementary controller based on approximate dynamic programming, which works alongside an existing power system controller. By introducing an action-dependent cost function as the optimization objective, the proposed OSLC is a nonidentifier-based method to provide an online optimal control adaptively as measurement data become available. The online learning of the OSLC enjoys the policy-search efficiency during policy iteration and the data efficiency of the least squares method. For the proposed OSLC, the stability of the controlled system during learning, the monotonic nature of the performance measure of the iterative supplementary controller, and the convergence of the iterative supplementary controller are proved. Furthermore, the efficacy of the proposed OSLC is demonstrated in a challenging power system frequency control problem in the presence of high penetration of wind generation.

  20. Metacognitive Control and Optimal Learning

    ERIC Educational Resources Information Center

    Son, Lisa K.; Sethi, Rajiv

    2006-01-01

    The notion of optimality is often invoked informally in the literature on metacognitive control. We provide a precise formulation of the optimization problem and show that optimal time allocation strategies depend critically on certain characteristics of the learning environment, such as the extent of time pressure, and the nature of the uptake…

  1. LATUX: An Iterative Workflow for Designing, Validating, and Deploying Learning Analytics Visualizations

    ERIC Educational Resources Information Center

    Martinez-Maldonado, Roberto; Pardo, Abelardo; Mirriahi, Negin; Yacef, Kalina; Kay, Judy; Clayphan, Andrew

    2015-01-01

    Designing, validating, and deploying learning analytics tools for instructors or students is a challenge that requires techniques and methods from different disciplines, such as software engineering, human-computer interaction, computer graphics, educational design, and psychology. Whilst each has established its own design methodologies, we now…

  2. The Iterative Design of a Mobile Learning Application to Support Scientific Inquiry

    ERIC Educational Resources Information Center

    Marty, Paul F.; Mendenhall, Anne; Douglas, Ian; Southerland, Sherry A.; Sampson, Victor; Kazmer, Michelle M.; Alemanne, Nicole; Clark, Amanda; Schellinger, Jennifer

    2013-01-01

    The ubiquity of mobile devices makes them well suited for field-based learning experiences that require students to gather data as part of the process of developing scientific inquiry practices. The usefulness of these devices, however, is strongly influenced by the nature of the applications students use to collect data in the field. To…

  3. Low-dose CT reconstruction via L1 dictionary learning regularization using iteratively reweighted least-squares.

    PubMed

    Zhang, Cheng; Zhang, Tao; Li, Ming; Peng, Chengtao; Liu, Zhaobang; Zheng, Jian

    2016-06-18

    In order to reduce the radiation dose of CT (computed tomography), compressed sensing theory has been a hot topic since it provides the possibility of a high quality recovery from the sparse sampling data. Recently, the algorithm based on DL (dictionary learning) was developed to deal with the sparse CT reconstruction problem. However, the existing DL algorithm focuses on the minimization problem with the L2-norm regularization term, which leads to reconstruction quality deteriorating while the sampling rate declines further. Therefore, it is essential to improve the DL method to meet the demand of more dose reduction. In this paper, we replaced the L2-norm regularization term with the L1-norm one. It is expected that the proposed L1-DL method could alleviate the over-smoothing effect of the L2-minimization and reserve more image details. The proposed algorithm solves the L1-minimization problem by a weighting strategy, solving the new weighted L2-minimization problem based on IRLS (iteratively reweighted least squares). Through the numerical simulation, the proposed algorithm is compared with the existing DL method (adaptive dictionary based statistical iterative reconstruction, ADSIR) and other two typical compressed sensing algorithms. It is revealed that the proposed algorithm is more accurate than the other algorithms especially when further reducing the sampling rate or increasing the noise. The proposed L1-DL algorithm can utilize more prior information of image sparsity than ADSIR. By transforming the L2-norm regularization term of ADSIR with the L1-norm one and solving the L1-minimization problem by IRLS strategy, L1-DL could reconstruct the image more exactly.

  4. Accurate in silico identification of protein succinylation sites using an iterative semi-supervised learning technique.

    PubMed

    Zhao, Xiaowei; Ning, Qiao; Chai, Haiting; Ma, Zhiqiang

    2015-06-07

    As a widespread type of protein post-translational modifications (PTMs), succinylation plays an important role in regulating protein conformation, function and physicochemical properties. Compared with the labor-intensive and time-consuming experimental approaches, computational predictions of succinylation sites are much desirable due to their convenient and fast speed. Currently, numerous computational models have been developed to identify PTMs sites through various types of two-class machine learning algorithms. These methods require both positive and negative samples for training. However, designation of the negative samples of PTMs was difficult and if it is not properly done can affect the performance of computational models dramatically. So that in this work, we implemented the first application of positive samples only learning (PSoL) algorithm to succinylation sites prediction problem, which was a special class of semi-supervised machine learning that used positive samples and unlabeled samples to train the model. Meanwhile, we proposed a novel succinylation sites computational predictor called SucPred (succinylation site predictor) by using multiple feature encoding schemes. Promising results were obtained by the SucPred predictor with an accuracy of 88.65% using 5-fold cross validation on the training dataset and an accuracy of 84.40% on the independent testing dataset, which demonstrated that the positive samples only learning algorithm presented here was particularly useful for identification of protein succinylation sites. Besides, the positive samples only learning algorithm can be applied to build predictors for other types of PTMs sites with ease. A web server for predicting succinylation sites was developed and was freely accessible at http://59.73.198.144:8088/SucPred/. Copyright © 2015 Elsevier Ltd. All rights reserved.

  5. Tests on a mock-up of the feedback controlled matching options of the ITER ICRH system

    SciTech Connect

    Grine, D.; Vervier, M.; Messiaen, A.; Dumortier, P.

    2009-11-26

    Automatic control of the matching of the ITER ICRH antenna array on a reference load is presently developed and tested for optimization on a low-powered scaled (1:5) mock-up. Resilience to fast load variations is obtained either by 4 Conjugate-T (CT) or 4 quadrature hybrid circuits, the latter being the reference option. The main results are (i) for the CT option: successful implementation of the simultaneous feedback control of 11 actuators for the matching of the 4 CT and for the control of the array toroidal phasing; (ii) for the hybrid option: the matching and the array current control via feedback control of the decouplers and double stub tuners. This system is being progressively implemented and the simultaneous control of matching and antenna current has already been successfully tested on half of the array for heating and current drive phasings.

  6. A time-domain decomposition iterative method for the solution of distributed linear quadratic optimal control problems

    NASA Astrophysics Data System (ADS)

    Heinkenschloss, Matthias

    2005-01-01

    We study a class of time-domain decomposition-based methods for the numerical solution of large-scale linear quadratic optimal control problems. Our methods are based on a multiple shooting reformulation of the linear quadratic optimal control problem as a discrete-time optimal control (DTOC) problem. The optimality conditions for this DTOC problem lead to a linear block tridiagonal system. The diagonal blocks are invertible and are related to the original linear quadratic optimal control problem restricted to smaller time-subintervals. This motivates the application of block Gauss-Seidel (GS)-type methods for the solution of the block tridiagonal systems. Numerical experiments show that the spectral radii of the block GS iteration matrices are larger than one for typical applications, but that the eigenvalues of the iteration matrices decay to zero fast. Hence, while the GS method is not expected to convergence for typical applications, it can be effective as a preconditioner for Krylov-subspace methods. This is confirmed by our numerical tests.A byproduct of this research is the insight that certain instantaneous control techniques can be viewed as the application of one step of the forward block GS method applied to the DTOC optimality system.

  7. Stimulus control and associative learning.

    PubMed Central

    Williams, B A

    1984-01-01

    Interest in operant research on stimulus control has declined at the same time that much interest has burgeoned in nonoperant areas. Several examples of this shift toward traditional learning theory are considered, all of which have sponsored theoretical approaches that attempt to characterize the underlying associative units. These theoretical approaches are defended on the grounds that they have generated a deeper understanding of a variety of often puzzling phenomena. My projection is that future research will be determined even more strongly by theories about the structure of associations. Particular issues for which such discussion will have major impact include (1) whether conditional stimulus control is qualitatively different than simpler forms of stimulus control, (2) whether stimulus control is organized hierarchically, and (3) the origin of categories of stimulus equivalence. PMID:6520579

  8. Enhanced confinement scenarios without large edge localized modes in tokamaks: control, performance, and extrapolability issues for ITER

    NASA Astrophysics Data System (ADS)

    Maingi, R.

    2014-11-01

    Large edge localized modes (ELMs) typically accompany good H-mode confinement in fusion devices, but can present problems for plasma facing components because of high transient heat loads. Here the range of techniques for ELM control deployed in fusion devices is reviewed. Two strategies in the ITER baseline design are emphasized: rapid ELM triggering and peak heat flux control via pellet injection, and the use of magnetic perturbations to suppress or mitigate ELMs. While both of these techniques are moderately well developed, with reasonable physical bases for projecting to ITER, differing observations between multiple devices are also discussed to highlight the needed community R&D. In addition, recent progress in ELM-free regimes, namely quiescent H-mode, I-mode, and enhanced pedestal H-mode is reviewed, and open questions for extrapolability are discussed. Finally progress and outstanding issues in alternate ELM control techniques are reviewed: supersonic molecular beam injection, edge electron cyclotron heating, lower hybrid heating and/or current drive, controlled periodic jogs of the vertical centroid position, ELM pace-making via periodic magnetic perturbations, ELM elimination with lithium wall conditioning, and naturally occurring small ELM regimes.

  9. Enhanced Confinement Scenarios Without Large Edge Localized Modes in Tokamaks: Control, Performance, and Extrapolability Issues for ITER

    SciTech Connect

    Maingi, R

    2014-07-01

    Large edge localized modes (ELMs) typically accompany good H-mode confinement in fusion devices, but can present problems for plasma facing components because of high transient heat loads. Here the range of techniques for ELM control deployed in fusion devices is reviewed. The two baseline strategies in the ITER baseline design are emphasized: rapid ELM triggering and peak heat flux control via pellet injection, and the use of magnetic perturbations to suppress or mitigate ELMs. While both of these techniques are moderately well developed, with reasonable physical bases for projecting to ITER, differing observations between multiple devices are also discussed to highlight the needed community R & D. In addition, recent progress in ELM-free regimes, namely Quiescent H-mode, I-mode, and Enhanced Pedestal H-mode is reviewed, and open questions for extrapolability are discussed. Finally progress and outstanding issues in alternate ELM control techniques are reviewed: supersonic molecular beam injection, edge electron cyclotron heating, lower hybrid heating and/or current drive, controlled periodic jogs of the vertical centroid position, ELM pace-making via periodic magnetic perturbations, ELM elimination with lithium wall conditioning, and naturally occurring small ELM regimes.

  10. A potentially robust plasma profile control approach for ITER using real-time estimation of linearized profile response models

    NASA Astrophysics Data System (ADS)

    Kim, S. H.; Lister, J. B.

    2012-07-01

    An active plasma profile control approach for ITER, which is potentially robust by being tolerant to changing and uncertain physics, has been explored in this work, using a technique based on real-time estimation of linearized profile response models. The linearized models approximate static responses of the plasma profiles to power changes in auxiliary heating and current drive systems. These models are updated in real-time, differing from the model-based technique which deduces a dynamic model from identification experiments. The underlying physics is simplified with several assumptions to allow real-time update of the profile response models; however, without significant loss of information necessary for feedback control of the plasma profiles. The response of the electron temperature profile is modelled by simplifying the electron heat transport equation. The response of the safety factor profile is computed by directly relating it to the changes in source current density profiles. The required actuator power changes are directly computed by inverting the response matrix using the singular value decomposition technique. The saturation of the actuator powers is taken into account and the capability of using quantized auxiliary powers is provided. The potential of our active control approach has been tested by applying it to simulations of the ITER hybrid mode operation using CRONOS. In these simulations, either a global transport model or a theory-based local transport model has been used and the electron temperature and safety factor profiles were well controlled either independently or simultaneously.

  11. Towards First-principles Control-oriented Modeling of the Magnetic and Kinetic Plasma Profile Evolutions in ITER

    NASA Astrophysics Data System (ADS)

    Barton, Justin E.; Schuster, Eugenio; Besseghir, Karim; Lister, Jonathan

    2012-10-01

    The ``hybrid'' and ``steady-state'' advanced scenarios are characterized by q profiles higher or equal to one to mitigate plasma instabilities and improve confinement, which are key for ITER to achieve its operational objectives. To achieve these scenarios, active model-based control of the current profile and thermal state of the plasma is required. Towards this goal, two control-oriented, plasma-response models are proposed. First, the poloidal flux diffusion equation is combined with empirical models of the electron density and temperature profiles, plasma resistivity, and non-inductive current drives to obtain a physics-based model of the poloidal flux and stored energy evolutions. Second, the empirical electron temperature model is replaced by the electron heat transport equation, which is combined with empirical models of the electron heat conductivity and heat sources to obtain a physics-based model of the poloidal flux and electron temperature evolutions. Simulation results comparing the evolution of the plasma parameters predicted by the control-oriented, physic-based models and the DINA-CH+CRONOS simulation code are presented for ITER, and the control objectives and challenges are discussed.

  12. Wall conditioning on ITER

    NASA Astrophysics Data System (ADS)

    Shimada, Michiya; Pitts, Richard A.

    2011-08-01

    Like all tokamaks, ITER will require wall conditioning systems and strategies for successful operation from the point of view of plasma-facing surface preparation. Unlike today's devices however, ITER will have to manage large quantities of tritium fuel, imposing on wall conditioning a major responsibility for tritium inventory control. It will also feature the largest plasma-facing beryllium surface ever used in a tokamak and its high duty cycle and long pulse are expected to lead to the rapid formation of deposited layers in which tritium can accumulate. This paper summarises the currently planned ITER wall conditioning systems and describes the strategy for their use throughout exploitation of the device.

  13. Control Theoretic Approach to Iterative Methods for Large-scale Toeplitz-type Systems with Application to Magnetic Field Analysis

    NASA Astrophysics Data System (ADS)

    Oda, Tomohito; Kashima, Kenji; Imura, Jun-Ichi; Miyazaki, Shuji; Morita, Hiroshi

    In this paper, stationary iterative methods for large-scale Toeplitz-type systems are investigated from a control theoretic point of view. We utilize spatially invariant structure of Toeplitz matrices, to avoid the curse of dimensionality arising in analysis and design of the convergence properties. Nonlinearities in the system are theoretically handled within the small gain and stability analysis for Lur'e systems. This theory enables us to achieve the desired global convergence of the proposed numerical scheme. We also evaluate the efficacy of the proposed method through an application to magnetic field analysis.

  14. Physics-based control-oriented modeling and robust feedback control of the plasma safety factor profile and stored energy dynamics in ITER

    NASA Astrophysics Data System (ADS)

    Barton, Justin E.; Besseghir, Karim; Lister, Jo; Schuster, Eugenio

    2015-11-01

    Many challenging plasma control problems still need to be addressed in order for the ITER plasma control system (PCS) to be able to maintain the plasma within a predefined operational space and optimize the plasma state evolution in the tokamak, which will greatly aid in the successful achievement of ITER’s goals. Firstly in this work, a general control-oriented, physics-based modeling approach is developed to obtain first-principles-driven (FPD) models of the plasma magnetic profile and stored energy evolutions valid for high performance, high confinement (H-mode) scenarios, with the goal of developing model-based closed-loop algorithms to control the safety factor profile (q profile) and stored energy evolutions in the tokamak. The FPD model is tailored to H-mode burning plasma scenarios in ITER by employing the DINA-CH & CRONOS free-boundary tokamak simulation code, and the FPD model’s prediction capabilities are demonstrated by comparing the prediction to data obtained from DINA-CH & CRONOS. Secondly, a model-based feedback control algorithm is designed to simultaneously track target q profile and stored energy evolutions in H-mode burning plasma scenarios in ITER by embedding the developed FPD model of the magnetic profile evolution into the control design process. The feedback controller is designed to ensure that the closed-loop system is robust to uncertainties in the electron density, electron temperature and plasma resistivity, and is tested in simulations with the developed FPD model. The effectiveness of the controller is demonstrated by first tracking nominal q profile and stored energy target evolutions, and then modulating the generated fusion power while maintaining the q profile in a stationary condition. In the process, many key practical issues for plasma profile control in ITER are investigated, which will be useful for the development of the ITER PCS that has recently been initiated. Some of the more pertinent investigated issues are the

  15. Tritium inventory control during ITER operation under carbon plasma-facing components by nitrogen-based plasma chemistry: a review

    NASA Astrophysics Data System (ADS)

    Tabarés, F. L.

    2013-06-01

    In spite of being highly suited for advanced plasma performance operation of tokamaks, as demonstrated over at least two decades of fusion plasma research, carbon is not currently considered as an integrating element of the plasma-facing components (PFCs) for the active phase of ITER. The main reason preventing its use under the very challenging scenarios foreseen in this phase, with edge-localized modes delivering several tens of MW m-2 to the divertor target every second or less, is the existing concern about reaching the tritium inventory value of 1000 g used in safety assessments in a time shorter than the projected lifetime of the divertor materials eroded by the plasma, set at 3000 shots. Although several mechanisms of tritium trapping in carbon components have been identified, co-deposition of the carbon radicals arising from chemically eroded chlorofluorocarbons in remote areas appears to play a dominant role. Several possible ways to keep control of the tritium build-up during the full operation of ITER have been put forward, mostly based on the periodic removal of the co-deposits by chemical (thermo-oxidation, plasma chemistry) or physical (laser, flash lamps) methods. In this work, we review the techniques for the inhibition and removal of tritium-rich co-deposits based on the strong chemical reactivity of some N-bearing molecules with carbon. The integration of these techniques into a possible scheme for tritium inventory control in the active phase of ITER under carbon-based PFCs with minimum down-time is discussed and the existing caveats are addressed.

  16. An iterative particle filter approach for coupled hydro-geophysical inversion of a controlled infiltration experiment

    SciTech Connect

    Manoli, Gabriele; Rossi, Matteo; Pasetto, Damiano; Deiana, Rita; Ferraris, Stefano; Cassiani, Giorgio; Putti, Mario

    2015-02-15

    The modeling of unsaturated groundwater flow is affected by a high degree of uncertainty related to both measurement and model errors. Geophysical methods such as Electrical Resistivity Tomography (ERT) can provide useful indirect information on the hydrological processes occurring in the vadose zone. In this paper, we propose and test an iterated particle filter method to solve the coupled hydrogeophysical inverse problem. We focus on an infiltration test monitored by time-lapse ERT and modeled using Richards equation. The goal is to identify hydrological model parameters from ERT electrical potential measurements. Traditional uncoupled inversion relies on the solution of two sequential inverse problems, the first one applied to the ERT measurements, the second one to Richards equation. This approach does not ensure an accurate quantitative description of the physical state, typically violating mass balance. To avoid one of these two inversions and incorporate in the process more physical simulation constraints, we cast the problem within the framework of a SIR (Sequential Importance Resampling) data assimilation approach that uses a Richards equation solver to model the hydrological dynamics and a forward ERT simulator combined with Archie's law to serve as measurement model. ERT observations are then used to update the state of the system as well as to estimate the model parameters and their posterior distribution. The limitations of the traditional sequential Bayesian approach are investigated and an innovative iterative approach is proposed to estimate the model parameters with high accuracy. The numerical properties of the developed algorithm are verified on both homogeneous and heterogeneous synthetic test cases based on a real-world field experiment.

  17. An iterative particle filter approach for coupled hydro-geophysical inversion of a controlled infiltration experiment

    NASA Astrophysics Data System (ADS)

    Manoli, Gabriele; Rossi, Matteo; Pasetto, Damiano; Deiana, Rita; Ferraris, Stefano; Cassiani, Giorgio; Putti, Mario

    2015-02-01

    The modeling of unsaturated groundwater flow is affected by a high degree of uncertainty related to both measurement and model errors. Geophysical methods such as Electrical Resistivity Tomography (ERT) can provide useful indirect information on the hydrological processes occurring in the vadose zone. In this paper, we propose and test an iterated particle filter method to solve the coupled hydrogeophysical inverse problem. We focus on an infiltration test monitored by time-lapse ERT and modeled using Richards equation. The goal is to identify hydrological model parameters from ERT electrical potential measurements. Traditional uncoupled inversion relies on the solution of two sequential inverse problems, the first one applied to the ERT measurements, the second one to Richards equation. This approach does not ensure an accurate quantitative description of the physical state, typically violating mass balance. To avoid one of these two inversions and incorporate in the process more physical simulation constraints, we cast the problem within the framework of a SIR (Sequential Importance Resampling) data assimilation approach that uses a Richards equation solver to model the hydrological dynamics and a forward ERT simulator combined with Archie's law to serve as measurement model. ERT observations are then used to update the state of the system as well as to estimate the model parameters and their posterior distribution. The limitations of the traditional sequential Bayesian approach are investigated and an innovative iterative approach is proposed to estimate the model parameters with high accuracy. The numerical properties of the developed algorithm are verified on both homogeneous and heterogeneous synthetic test cases based on a real-world field experiment.

  18. Safety-factor profile tailoring by improved electron cyclotron system for sawtooth control and reverse shear scenarios in ITER

    SciTech Connect

    Zucca, C.; Sauter, O.; Fable, E.; Henderson, M. A.; Polevoi, A.; Saibene, G.

    2008-11-01

    The effect of the predicted local electron cyclotron current driven by the optimized electron cyclotron system on ITER is discussed. A design variant was recently proposed to enlarge the physics program covered by the upper and equatorial launchers. By extending the functionality range of the upper launcher, significant control capabilities of the sawtooth period can be obtained. The upper launcher improvement still allows enough margin to exceed the requirements for neoclassical tearing mode stabilization, for which it was originally designed. The analysis of the sawtooth control is carried on with the ASTRA transport code, coupled with the threshold model by Por-celli, to study the control capabilities of the improved upper launcher on the sawtooth instability. The simulations take into account the significant stabilizing effect of the fusion alpha particles. The sawtooth period can be increased by a factor of 1.5 with co-ECCD outside the q = 1 surface, and decreased by at least 30% with co-ECCD inside q = 1. The present ITER base-line design has the electron cyclotron launchers providing only co-ECCD. The variant for the equatorial launcher proposes the possibility to drive counter-ECCD with 1 of the 3 rows of mirrors: the counter-ECCD can then be balanced with co-ECCD and provide pure ECH with no net driven current. The difference between full co-ECCD off-axis using all 20MW from the equatorial launcher and 20MW co-ECCD driven by 2/3 from the equatorial launcher and 1/3 from the upper launcher is shown to be negligible. Cnt-ECCD also offers greater control of the plasma current density, therefore this analysis addresses the performance of the equatorial launcher to control the central q profile. The equatorial launcher is shown to control very efficiently the value of q{sub 0.2}-q{sub min} in advanced scenarios, if one row provides counter-ECCD.

  19. A Non-iterative Scheme for Orthogonal Grid Generation with Control Function and Specified Boundary Correspondence on Three Sides

    NASA Astrophysics Data System (ADS)

    Oh, H. J.; Kang, I. S.

    1994-05-01

    A new numerical scheme is proposed for generating an orthogonal grid in a simply-connected 2D domain. The scheme is based on the idea of decomposition of a global orthogonal transform into consecutive mappings of a conformal mapping and an auxiliary orthogonal mapping, which was suggested by Kang and Leal (J, Comput. Phys.102, 78 (1992)). The method is non-iterative and flexible in the adjustment of grid spacing The grid spacing can be controlled mainly by specification of the boundary correspondence up to three sides of the boundary. The method is also equipped with a control function that provides further degrees of freedom in the grid spacing adjustment. From a mathematical viewpoint, the proposed scheme can also be regarded as a numerical implementation of the constructive proof for the existence of a solution of the orthogonal mapping problem in an arbitrary simply-connected domain under the condition that the boundary correspondence is specified on three sides.

  20. A non-iterative scheme for orthogonal grid generation with control function and specified boundary correspondence on three sides

    NASA Astrophysics Data System (ADS)

    Oh, H. J.; Kang, I. S.

    1994-05-01

    A new numerical scheme is proposed for generating an orthogonal grid in a simply-connected 2D domain. The scheme is based on the idea of decomposition of a global orthogonal transform into consecutive mappings of a conformal mapping and an auxiliary orthogonal mapping which was suggested by Kang and Leal (J. Comput. Phys. 102, 78 (1992)). The method is non-iterative and flexible in the adjustment of grid spacing. The grid spacing can be controlled mainly by specification of the boundary correspondence up to three sides of the boundary. The method is also equipped with a control function that provides further degrees of freedom in the grid spacing adjustment. From a mathematical viewpoint, the proposed scheme can also be regarded as a numerical implementation of the constructive proof for the existence of a solution of the orthogonal mapping problem in an arbitrary simply-connected domain under the condition that the boundary correspondence is specified on three sides.

  1. Multimachine Data–Based Prediction of High-Frequency Sensor Signal Noise for Resistive Wall Mode Control in ITER

    SciTech Connect

    Liu, Yueqiang; Sabbagh, S. A.; Chapman, I. T.; Gerasimov, S.; Gribov, Y.; Hender, T. C.; Igochine, V.; Maraschek, M.; Matsunaga, G.; Okabayashi, M.; Strait, E. J.

    2016-11-01

    The high frequency noise, above the frequency of typical resistive wall modes, from magnetic pickup coils is analysed across a range of present tokamak devices including DIII-D, JET, MAST, ASDEX Upgrade, JT-60U and NSTX. Application of a high-pass filter enables identification of the noise component with Gaussian-like statistics, that shares certain common characteristics in all devices considered. A conservative prediction is made for ITER plasmas based on the multi-machine database, for the high-frequency noise component of the sensor signals, for the purpose of feedback stabilisation of the resistive wall mode. The predicted root-mean-square n=1 (n is the toroidal mode number) noise level is 104 -105Gauss/second for the voltage signal, and 0.1-1Gauss for the perturbed magnetic field signal. The lower cutoff frequency of the Gaussian pickup noise scales linearly with the sampling frequency, with the scaling coefficient of about 0.1. These basic noise characteristics should be useful for the modelling based design of the feedback control system for the resistive wall mode in ITER.

  2. Multimachine Data–Based Prediction of High-Frequency Sensor Signal Noise for Resistive Wall Mode Control in ITER

    DOE PAGES

    Liu, Yueqiang; Sabbagh, S. A.; Chapman, I. T.; ...

    2016-11-01

    The high frequency noise, above the frequency of typical resistive wall modes, from magnetic pickup coils is analysed across a range of present tokamak devices including DIII-D, JET, MAST, ASDEX Upgrade, JT-60U and NSTX. Application of a high-pass filter enables identification of the noise component with Gaussian-like statistics, that shares certain common characteristics in all devices considered. A conservative prediction is made for ITER plasmas based on the multi-machine database, for the high-frequency noise component of the sensor signals, for the purpose of feedback stabilisation of the resistive wall mode. The predicted root-mean-square n=1 (n is the toroidal mode number)more » noise level is 104 -105Gauss/second for the voltage signal, and 0.1-1Gauss for the perturbed magnetic field signal. The lower cutoff frequency of the Gaussian pickup noise scales linearly with the sampling frequency, with the scaling coefficient of about 0.1. These basic noise characteristics should be useful for the modelling based design of the feedback control system for the resistive wall mode in ITER.« less

  3. Multimachine data–based prediction of high-frequency sensor signal noise for resistive wall mode control in ITER

    SciTech Connect

    Liu, Yueqiang; Sabbagh, S. A.; Chapman, I. T.; Gerasimov, S.; Gribov, Y.; Hender, T. C.; Igochine, V.; Maraschek, M.; Matsunaga, G.; Okabayashi, M.; Strait, E. J.

    2016-08-12

    The high-frequency noise measured by magnetic sensors, at levels above the typical frequency of resistive wall modes, is analyzed across a range of present tokamak devices including DIII-D, JET, MAST, ASDEX Upgrade, JT-60U, and NSTX. A high-pass filter enables identification of the noise component with Gaussian-like statistics that shares certain common characteristics in all devices considered. A conservative prediction is made for ITER plasma operation of the high-frequency noise component of the sensor signals, to be used for resistive wall mode feedback stabilization, based on the multimachine database. The predicted root-mean-square n = 1 (n is the toroidal mode number) noise level is 104 to 105 G/s for the voltage signal, and 0.1 to 1 G for the perturbed magnetic field signal. The lower cutoff frequency of the Gaussian pickup noise scales linearly with the sampling frequency, with a scaling coefficient of about 0.1. As a result, these basic noise characteristics should be useful for the modeling-based design of the feedback control system for the resistive wall mode in ITER.

  4. Multimachine data–based prediction of high-frequency sensor signal noise for resistive wall mode control in ITER

    DOE PAGES

    Liu, Yueqiang; Sabbagh, S. A.; Chapman, I. T.; ...

    2016-08-12

    The high-frequency noise measured by magnetic sensors, at levels above the typical frequency of resistive wall modes, is analyzed across a range of present tokamak devices including DIII-D, JET, MAST, ASDEX Upgrade, JT-60U, and NSTX. A high-pass filter enables identification of the noise component with Gaussian-like statistics that shares certain common characteristics in all devices considered. A conservative prediction is made for ITER plasma operation of the high-frequency noise component of the sensor signals, to be used for resistive wall mode feedback stabilization, based on the multimachine database. The predicted root-mean-square n = 1 (n is the toroidal mode number)more » noise level is 104 to 105 G/s for the voltage signal, and 0.1 to 1 G for the perturbed magnetic field signal. The lower cutoff frequency of the Gaussian pickup noise scales linearly with the sampling frequency, with a scaling coefficient of about 0.1. As a result, these basic noise characteristics should be useful for the modeling-based design of the feedback control system for the resistive wall mode in ITER.« less

  5. Simultaneous gains tuning in boiler/turbine PID-based controller clusters using iterative feedback tuning methodology.

    PubMed

    Zhang, Shu; Taft, Cyrus W; Bentsman, Joseph; Hussey, Aaron; Petrus, Bryan

    2012-09-01

    Tuning a complex multi-loop PID based control system requires considerable experience. In today's power industry the number of available qualified tuners is dwindling and there is a great need for better tuning tools to maintain and improve the performance of complex multivariable processes. Multi-loop PID tuning is the procedure for the online tuning of a cluster of PID controllers operating in a closed loop with a multivariable process. This paper presents the first application of the simultaneous tuning technique to the multi-input-multi-output (MIMO) PID based nonlinear controller in the power plant control context, with the closed-loop system consisting of a MIMO nonlinear boiler/turbine model and a nonlinear cluster of six PID-type controllers. Although simplified, the dynamics and cross-coupling of the process and the PID cluster are similar to those used in a real power plant. The particular technique selected, iterative feedback tuning (IFT), utilizes the linearized version of the PID cluster for signal conditioning, but the data collection and tuning is carried out on the full nonlinear closed-loop system. Based on the figure of merit for the control system performance, the IFT is shown to deliver performance favorably comparable to that attained through the empirical tuning carried out by an experienced control engineer.

  6. Connectionist Learning Control at GTE Laboratories

    NASA Astrophysics Data System (ADS)

    Franklin, Judy A.; Sutton, Richard S.; Anderson, Charles W.; Selfridge, Oliver G.; Schwartz, Daniel B.

    1990-02-01

    At GTE Laboratories, we are advancing the theory of connectionist learning architectures for real-time control while exploring their relationships to animal learning models, applications in manufacturing quality control, and VLSI implementations. We seek connectionist-network architectures with improved convergence rate and scaling properties, as assessed on simulated and actual control problems. Our primary focus is on extensions to reinforcement learning. These include adaptive critics, feature/representation adaptation in multilayer networks, hybrid connectionist/conventional controllers, and modular networks for hierarchical control. We are also extending methods for system identification, or model learning, to include internal models learned using temporal-differences. We propose the integration of reinforcement and model learning based on their relationships to dynamic programming. We are working to resolve how connectionist systems should serve as a total systems concept or as tools in a larger architecture.

  7. An adaptive learning control system for aircraft

    NASA Technical Reports Server (NTRS)

    Mekel, R.; Nachmias, S.

    1976-01-01

    A learning control system is developed which blends the gain scheduling and adaptive control into a single learning system that has the advantages of both. An important feature of the developed learning control system is its capability to adjust the gain schedule in a prescribed manner to account for changing aircraft operating characteristics. Furthermore, if tests performed by the criteria of the learning system preclude any possible change in the gain schedule, then the overall system becomes an ordinary gain scheduling system. Examples are discussed.

  8. Design of an iterative auto-tuning algorithm for a fuzzy PID controller

    NASA Astrophysics Data System (ADS)

    Saeed, Bakhtiar I.; Mehrdadi, B.

    2012-05-01

    Since the first application of fuzzy logic in the field of control engineering, it has been extensively employed in controlling a wide range of applications. The human knowledge on controlling complex and non-linear processes can be incorporated into a controller in the form of linguistic terms. However, with the lack of analytical design study it is becoming more difficult to auto-tune controller parameters. Fuzzy logic controller has several parameters that can be adjusted, such as: membership functions, rule-base and scaling gains. Furthermore, it is not always easy to find the relation between the type of membership functions or rule-base and the controller performance. This study proposes a new systematic auto-tuning algorithm to fine tune fuzzy logic controller gains. A fuzzy PID controller is proposed and applied to several second order systems. The relationship between the closed-loop response and the controller parameters is analysed to devise an auto-tuning method. The results show that the proposed method is highly effective and produces zero overshoot with enhanced transient response. In addition, the robustness of the controller is investigated in the case of parameter changes and the results show a satisfactory performance.

  9. A Reinforcement Learning Approach to Control.

    DTIC Science & Technology

    1997-05-31

    acquisition is inherently a partially observable Markov decision problem. This report describes an efficient, scalable reinforcement learning approach to the...deployment of refined intelligent gaze control techniques. This report first lays a theoretical foundation for reinforcement learning . It then introduces...perform well in both high and low SNR ATR environments. Reinforcement learning coupled with history features appears to be both a sound foundation and a practical scalable base for gaze control.

  10. Vehicle Steering control: A model of learning

    NASA Technical Reports Server (NTRS)

    Smiley, A.; Reid, L.; Fraser, M.

    1978-01-01

    A hierarchy of strategies were postulated to describe the process of learning steering control. Vehicle motion and steering control data were recorded for twelve novices who drove an instrumented car twice a week during and after a driver training course. Car-driver describing functions were calculated, the probable control structure determined, and the driver-alone transfer function modelled. The data suggested that the largest changes in steering control with learning were in the way the driver used the lateral position cue.

  11. Indirect learning control for nonlinear dynamical systems

    NASA Technical Reports Server (NTRS)

    Ryu, Yeong Soon; Longman, Richard W.

    1993-01-01

    In a previous paper, learning control algorithms were developed based on adaptive control ideas for linear time variant systems. The learning control methods were shown to have certain advantages over their adaptive control counterparts, such as the ability to produce zero tracking error in time varying systems, and the ability to eliminate repetitive disturbances. In recent years, certain adaptive control algorithms have been developed for multi-body dynamic systems such as robots, with global guaranteed convergence to zero tracking error for the nonlinear system euations. In this paper we study the relationship between such adaptive control methods designed for this specific class of nonlinear systems, and the learning control problem for such systems, seeking to converge to zero tracking error in following a specific command repeatedly, starting from the same initial conditions each time. The extension of these methods from the adaptive control problem to the learning control problem is seen to be trivial. The advantages and disadvantages of using learning control based on such adaptive control concepts for nonlinear systems, and the use of other currently available learning control algorithms are discussed.

  12. Communication Requirements in the Cooperative Control of Wide Area Search Munitions Using Iterative Network Flow

    DTIC Science & Technology

    2004-02-01

    both centralized and redun- dant, i.e. each vehicle computes its own network flow. Momentarily disregarding communication issues , the problem, in general...Control Con- ference, 2003. [5] Jason W. Mitchell and Andrew G. Sparks. Communication issues in the cooperative control of unmanned aerial vehicles. In

  13. Iterative control of Görtler vortices via local wall deformations

    NASA Astrophysics Data System (ADS)

    Sescu, Adrian; Taoudi, Lamiae; Afsar, Mohammed

    2017-06-01

    Görtler vortices develop along concave walls as a result of the imbalance between the centrifugal force and radial pressure gradient. In this study, we introduce a simple control strategy aimed at reducing the growth rate of Görtler vortices by locally modifying the surface geometry in spanwise and streamwise directions. Such wall deformations are accounted in the boundary region equations by using a Prandtl transform of dependent and independent variables. The vortex energy is then controlled via a classical proportional control algorithm for which either the wall-normal velocity or the wall shear stress serves as the control variable. Our numerical results indicate that the control algorithm is quite effective in minimizing the wall shear stress.

  14. Analysis of the phase control of the ITER ICRH antenna array. Influence on the load resilience and radiated power spectrum

    NASA Astrophysics Data System (ADS)

    Messiaen, A.; Swain, D.; Ongena, J.; Vervier, M.

    2015-12-01

    The paper analyses how the phasing of the ITER ICRH 24 strap array evolves from the power sources up to the strap currents of the antenna. The study of the phasing control and coherence through the feeding circuits with prematching and automatic matching and decoupling network is made by modeling starting from the TOPICA matrix of the antenna array for a low coupling plasma profile and for current drive phasing (worst case for mutual coupling effects). The main results of the analysis are: (i) the strap current amplitude is well controlled by the antinode Vmax amplitude of the feeding lines, (ii) the best toroidal phasing control is done by the adjustment of the mean phase of Vmax of each poloidal straps column, (iii) with well adjusted system the largest strap current phasing error is ±20°, (iv) the effect on load resilience remains well below the maximum affordable VSWR of the generators, (v) the effect on the radiated power spectrum versus k// computed by means of the coupling code ANTITER II remains small for the considered cases.

  15. Analysis of the phase control of the ITER ICRH antenna array. Influence on the load resilience and radiated power spectrum

    SciTech Connect

    Messiaen, A. Ongena, J.; Vervier, M.; Swain, D.

    2015-12-10

    The paper analyses how the phasing of the ITER ICRH 24 strap array evolves from the power sources up to the strap currents of the antenna. The study of the phasing control and coherence through the feeding circuits with prematching and automatic matching and decoupling network is made by modeling starting from the TOPICA matrix of the antenna array for a low coupling plasma profile and for current drive phasing (worst case for mutual coupling effects). The main results of the analysis are: (i) the strap current amplitude is well controlled by the antinode V{sub max} amplitude of the feeding lines, (ii) the best toroidal phasing control is done by the adjustment of the mean phase of V{sub max} of each poloidal straps column, (iii) with well adjusted system the largest strap current phasing error is ±20°, (iv) the effect on load resilience remains well below the maximum affordable VSWR of the generators, (v) the effect on the radiated power spectrum versus k{sub //} computed by means of the coupling code ANTITER II remains small for the considered cases.

  16. Analysis of the phase control of the ITER ICRH antenna array. Influence on the load resilience and radiated power spectrum

    SciTech Connect

    Messiaen, Andre; Swain, David W; Ongena, Jef; Vervier, Michael

    2015-01-01

    The paper analyses how the phasing of the ITER ICRH 24 strap array evolves from the power sources up to the strap currents of the antenna. The study of the phasing control and coherence through the feeding circuits with prematching and automatic matching and decoupling network is made by modeling starting from the TOPICA matrix of the antenna array for a low coupling plasma profile and for current drive phasing (worst case for mutual coupling effects). The main results of the analysis are: (i) the strap current amplitude is well controlled by the antinode V-max amplitude of the feeding lines, (ii) the best toroidal phasing control is done by the adjustment of the mean phase of V-max of each poloidal straps column, (iii) with well adjusted system the largest strap current phasing error is +/- 20 degrees, (iv) the effect on load resilience remains well below the maximum affordable VSWR of the generators, (v) the effect on the radiated power spectrum versus k//computed by means of the coupling code ANTITER II remains small for the considered cases. [GRAPHICS] .

  17. Nearly data-based optimal control for linear discrete model-free systems with delays via reinforcement learning

    NASA Astrophysics Data System (ADS)

    Zhang, Jilie; Zhang, Huaguang; Wang, Binrui; Cai, Tiaoyang

    2016-05-01

    In this paper, a nearly data-based optimal control scheme is proposed for linear discrete model-free systems with delays. The nearly optimal control can be obtained using only measured input/output data from systems, by reinforcement learning technology, which combines Q-learning with value iterative algorithm. First, we construct a state estimator by using the measured input/output data. Second, the quadratic functional is used to approximate the value function at each point in the state space, and the data-based control is designed by Q-learning method using the obtained state estimator. Then, the paper states the method, that is, how to solve the optimal inner kernel matrix ? in the least-square sense, by value iteration algorithm. Finally, the numerical examples are given to illustrate the effectiveness of our approach.

  18. Fault Tolerant Parallel Implementations of Iterative Algorithms for Optimal Control Problems

    DTIC Science & Technology

    1988-01-21

    recurrence systems. IEEE Trans. Comput. C-24, 7 (July 1975). 701- 𔃻 4. Chen. S. C., Kuck. D. J.. and Sameh , A. H. Practical parallcl band triangular...solution of first-order linear recurrences. Proc ird.nnua.4llerton Cw)cr’nce ,) (.’) pi1( aw,. Control and Computing. Oct. 2-4. 1985. pp. 243-250. 10. Sameh

  19. A quasi-analytical method for non-iterative computation of nonlinear controls

    NASA Technical Reports Server (NTRS)

    Junkins, J. L.; Thompson, R. C.; Turner, J. D.

    1987-01-01

    An optimal control solution process was developed for a general class of nonlinear dynamical systems. The method combines control theory, perturbation methods, and Van Loan's recent matrix exponential results. A variety of applications support the practical utility of this method. Nonlinear rigid body optimal maneuvers are routinely solved. Flexible body dynamical systems of an order greater than 40 were solved. The method fails occasionally due to poor convergence of the perturbation expansion or numerical difficulties associated with computing the matrix exponential. The method is attractive because it appears to be a good candidate for semi-automation; no initial guess is required, and it usually converges at 2nd or 3rd order in minutes of machine time.

  20. Air pollution control system research: An iterative approach to developing affordable systems

    NASA Technical Reports Server (NTRS)

    Watt, Lewis C.; Cannon, Fred S.; Heinsohn, Robert J.; Spaeder, Timothy A.

    1995-01-01

    This paper describes a Strategic Environmental Research and Development Program (SERDP) funded project led jointly by the Marine Corps Multi-Commodity Maintenance Centers, and the Air and Energy Engineering Research Laboratory (AEERL) of the USEPA. The research focuses on paint booth exhaust minimization using recirculation, and on volatile organic compound (VOC) oxidation by the modules of a hybrid air pollution control system. The research team is applying bench, pilot and full scale systems to accomplish the goals of reduced cost and improved effectiveness of air treatment systems for paint booth exhaust.

  1. Air pollution control system research: An iterative approach to developing affordable systems

    SciTech Connect

    Watt, L.C.; Cannon, F.S.; Heinsohn, R.J.; Spaeder, T.A.; Darvin, C.H.

    1993-12-31

    The research will be accomplished on lab scale, pilot scale, and production air pollution control systems (APCS). The production system, to be installed at Marine Corps Logistics Base (MCLB) Barstow, CA, will treat the exhaust from three paint booths which will be modified to recirculate a large percentage of their exhaust. These recirculation systems are, themselves, a critical element in the overall R and D effort. The goal of the program is to conduct an R and D effort which will improve and demonstrate a combination of technologies intended to make VOC treatment both effective and affordable. The US Marine Corps, the other services and industry will each benefit.

  2. Version Control in Project-Based Learning

    ERIC Educational Resources Information Center

    Milentijevic, Ivan; Ciric, Vladimir; Vojinovic, Oliver

    2008-01-01

    This paper deals with the development of a generalized model for version control systems application as a support in a range of project-based learning methods. The model is given as UML sequence diagram and described in detail. The proposed model encompasses a wide range of different project-based learning approaches by assigning a supervisory…

  3. Version Control in Project-Based Learning

    ERIC Educational Resources Information Center

    Milentijevic, Ivan; Ciric, Vladimir; Vojinovic, Oliver

    2008-01-01

    This paper deals with the development of a generalized model for version control systems application as a support in a range of project-based learning methods. The model is given as UML sequence diagram and described in detail. The proposed model encompasses a wide range of different project-based learning approaches by assigning a supervisory…

  4. Locus of Control, Social Class, and Learning.

    ERIC Educational Resources Information Center

    Vasquez, James A.

    The relationship between locus of control, social class, and learning processes were reviewed by analyzing: (1) externality as a function of socioeconomic status, i.e., the lower the status, the greater the degree of externality; (2) the impact of the early home environment on the child's learning and development; (3) classroom and teacher…

  5. A Case Study of Modern PLC and LabVIEW Controls: Power Supply Controls for the ORNL ITER ECH Test Stand

    SciTech Connect

    Barker, Alan M; Killough, Stephen M; Bigelow, Tim S; White, John A; Munro Jr, John K

    2011-01-01

    Power Supply Controls are being developed at Oak Ridge National Laboratory (ORNL) to test transmission line components of the Electron Cyclotron Heating (ECH) system, with a focus on gyrotrons and waveguides, in support of the International Thermonuclear Experimental Reactor (ITER). The control is performed by several Programmable Logic Controllers (PLC s) located near the different equipment. A technique of Supervisory Control and Data Acquisition (SCADA) is presented to monitor, control, and log actions of the PLC s on a PC through use of Allen Bradley s Remote I/O communication interface coupled with an Open Process Control/Object Linking and Embedding [OLE] for Process Control (OPC) Server/Client architecture. The OPC data is then linked to a National Instruments (NI) LabVIEW system for monitoring and control. Details of the architecture and insight into applicability to other systems are presented in the rest of this paper. Future integration with an EPICS (Experimental Physics Industrial Control System) based mini-CODAC (Control, Data Access and Communication) SCADA system is under consideration, and integration considerations will be briefly introduced.

  6. Learning styles: The learning methods of air traffic control students

    NASA Astrophysics Data System (ADS)

    Jackson, Dontae L.

    In the world of aviation, air traffic controllers are an integral part in the overall level of safety that is provided. With a number of controllers reaching retirement age, the Air Traffic Collegiate Training Initiative (AT-CTI) was created to provide a stronger candidate pool. However, AT-CTI Instructors have found that a number of AT-CTI students are unable to memorize types of aircraft effectively. This study focused on the basic learning styles (auditory, visual, and kinesthetic) of students and created a teaching method to try to increase memorization in AT-CTI students. The participants were asked to take a questionnaire to determine their learning style. Upon knowing their learning styles, participants attended two classroom sessions. The participants were given a presentation in the first class, and divided into a control and experimental group for the second class. The control group was given the same presentation from the first classroom session while the experimental group had a group discussion and utilized Middle Tennessee State University's Air Traffic Control simulator to learn the aircraft types. Participants took a quiz and filled out a survey, which tested the new teaching method. An appropriate statistical analysis was applied to determine if there was a significant difference between the control and experimental groups. The results showed that even though the participants felt that the method increased their learning, there was no significant difference between the two groups.

  7. Off-policy integral reinforcement learning optimal tracking control for continuous-time chaotic systems

    NASA Astrophysics Data System (ADS)

    Wei, Qing-Lai; Song, Rui-Zhuo; Sun, Qiu-Ye; Xiao, Wen-Dong

    2015-09-01

    This paper estimates an off-policy integral reinforcement learning (IRL) algorithm to obtain the optimal tracking control of unknown chaotic systems. Off-policy IRL can learn the solution of the HJB equation from the system data generated by an arbitrary control. Moreover, off-policy IRL can be regarded as a direct learning method, which avoids the identification of system dynamics. In this paper, the performance index function is first given based on the system tracking error and control error. For solving the Hamilton-Jacobi-Bellman (HJB) equation, an off-policy IRL algorithm is proposed. It is proven that the iterative control makes the tracking error system asymptotically stable, and the iterative performance index function is convergent. Simulation study demonstrates the effectiveness of the developed tracking control method. Project supported by the National Natural Science Foundation of China (Grant Nos. 61304079 and 61374105), the Beijing Natural Science Foundation, China (Grant Nos. 4132078 and 4143065), the China Postdoctoral Science Foundation (Grant No. 2013M530527), the Fundamental Research Funds for the Central Universities, China (Grant No. FRF-TP-14-119A2), and the Open Research Project from State Key Laboratory of Management and Control for Complex Systems, China (Grant No. 20150104).

  8. Modeling of divertor particle and heat loads during application of resonant magnetic perturbation fields for ELM control in ITER

    NASA Astrophysics Data System (ADS)

    Schmitz, O.; Becoulet, M.; Cahyna, P.; Evans, T. E.; Feng, Y.; Frerichs, H.; Kirschner, A.; Kukushkin, A.; Laengner, R.; Lunt, T.; Loarte, A.; Pitts, R.; Reiser, D.; Reiter, D.; Saibene, G.; Samm, U.

    2013-07-01

    First results from three-dimensional modeling of the divertor heat and particle flux pattern during application of resonant magnetic perturbation fields as ELM control scheme in ITER with the EMC3-Eirene fluid plasma and kinetic neutral transport code are discussed. The formation of a helical magnetic footprint breaks the toroidal symmetry of the heat and particle fluxes. Expansion of the flux pattern as far as 60 cm away from the unperturbed strike line is seen with vacuum RMP fields, resulting in a preferable heat flux spreading. Inclusion of plasma response reduces the radial extension of the heat and particle fluxes and results in a heat flux peaking closer to the unperturbed level. A strong reduction of the particle confinement is found. 3D flow channels are identified as a consistent reason due to direct parallel outflow from inside of the separatrix. Their radial inward expansion and hence the level of particle pump out is shown to be dependent on the perturbation level.

  9. Refining fuzzy logic controllers with machine learning

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1994-01-01

    In this paper, we describe the GARIC (Generalized Approximate Reasoning-Based Intelligent Control) architecture, which learns from its past performance and modifies the labels in the fuzzy rules to improve performance. It uses fuzzy reinforcement learning which is a hybrid method of fuzzy logic and reinforcement learning. This technology can simplify and automate the application of fuzzy logic control to a variety of systems. GARIC has been applied in simulation studies of the Space Shuttle rendezvous and docking experiments. It has the potential of being applied in other aerospace systems as well as in consumer products such as appliances, cameras, and cars.

  10. Learning to Control Advanced Life Support Systems

    NASA Technical Reports Server (NTRS)

    Subramanian, Devika

    2004-01-01

    Advanced life support systems have many interacting processes and limited resources. Controlling and optimizing advanced life support systems presents unique challenges. In particular, advanced life support systems are nonlinear coupled dynamical systems and it is difficult for humans to take all interactions into account to design an effective control strategy. In this project. we developed several reinforcement learning controllers that actively explore the space of possible control strategies, guided by rewards from a user specified long term objective function. We evaluated these controllers using a discrete event simulation of an advanced life support system. This simulation, called BioSim, designed by Nasa scientists David Kortenkamp and Scott Bell has multiple, interacting life support modules including crew, food production, air revitalization, water recovery, solid waste incineration and power. They are implemented in a consumer/producer relationship in which certain modules produce resources that are consumed by other modules. Stores hold resources between modules. Control of this simulation is via adjusting flows of resources between modules and into/out of stores. We developed adaptive algorithms that control the flow of resources in BioSim. Our learning algorithms discovered several ingenious strategies for maximizing mission length by controlling the air and water recycling systems as well as crop planting schedules. By exploiting non-linearities in the overall system dynamics, the learned controllers easily out- performed controllers written by human experts. In sum, we accomplished three goals. We (1) developed foundations for learning models of coupled dynamical systems by active exploration of the state space, (2) developed and tested algorithms that learn to efficiently control air and water recycling processes as well as crop scheduling in Biosim, and (3) developed an understanding of the role machine learning in designing control systems for

  11. A learning controller for nonrepetitive robotic operation

    NASA Technical Reports Server (NTRS)

    Miller, W. T., III

    1987-01-01

    A practical learning control system is described which is applicable to complex robotic and telerobotic systems involving multiple feedback sensors and multiple command variables. In the controller, the learning algorithm is used to learn to reproduce the nonlinear relationship between the sensor outputs and the system command variables over particular regions of the system state space, rather than learning the actuator commands required to perform a specific task. The learned information is used to predict the command signals required to produce desired changes in the sensor outputs. The desired sensor output changes may result from automatic trajectory planning or may be derived from interactive input from a human operator. The learning controller requires no a priori knowledge of the relationships between the sensor outputs and the command variables. The algorithm is well suited for real time implementation, requiring only fixed point addition and logical operations. The results of learning experiments using a General Electric P-5 manipulator interfaced to a VAX-11/730 computer are presented. These experiments involved interactive operator control, via joysticks, of the position and orientation of an object in the field of view of a video camera mounted on the end of the robot arm.

  12. Discrete time learning control in nonlinear systems

    NASA Technical Reports Server (NTRS)

    Longman, Richard W.; Chang, Chi-Kuang; Phan, Minh

    1992-01-01

    In this paper digital learning control methods are developed primarily for use in single-input, single-output nonlinear dynamic systems. Conditions for convergence of the basic form of learning control based on integral control concepts are given, and shown to be satisfied by a large class of nonlinear problems. It is shown that it is not the gross nonlinearities of the differential equations that matter in the convergence, but rather the much smaller nonlinearities that can manifest themselves during the short time interval of one sample time. New algorithms are developed that eliminate restrictions on the size of the learning gain, and on knowledge of the appropriate sign of the learning gain, for convergence to zero error in tracking a feasible desired output trajectory. It is shown that one of the new algorithms can give guaranteed convergence in the presence of actuator saturation constraints, and indicate when the requested trajectory is beyond the actuator capabilities.

  13. An adaptive learning control system for aircraft

    NASA Technical Reports Server (NTRS)

    Mekel, R.; Nachmias, S.

    1978-01-01

    A learning control system and its utilization as a flight control system for F-8 Digital Fly-By-Wire (DFBW) research aircraft is studied. The system has the ability to adjust a gain schedule to account for changing plant characteristics and to improve its performance and the plant's performance in the course of its own operation. Three subsystems are detailed: (1) the information acquisition subsystem which identifies the plant's parameters at a given operating condition; (2) the learning algorithm subsystem which relates the identified parameters to predetermined analytical expressions describing the behavior of the parameters over a range of operating conditions; and (3) the memory and control process subsystem which consists of the collection of updated coefficients (memory) and the derived control laws. Simulation experiments indicate that the learning control system is effective in compensating for parameter variations caused by changes in flight conditions.

  14. Real-time control of divertor detachment in H-mode with impurity seeding using Langmuir probe feedback in JET-ITER-like wall

    NASA Astrophysics Data System (ADS)

    Guillemaut, C.; Lennholm, M.; Harrison, J.; Carvalho, I.; Valcarcel, D.; Felton, R.; Griph, S.; Hogben, C.; Lucock, R.; Matthews, G. F.; Perez Von Thun, C.; Pitts, R. A.; Wiesen, S.; contributors, JET

    2017-04-01

    Burning plasmas with 500 MW of fusion power on ITER will rely on partially detached divertor operation to keep target heat loads at manageable levels. Such divertor regimes will be maintained by a real-time control system using the seeding of radiative impurities like nitrogen (N), neon or argon as actuator and one or more diagnostic signals as sensors. Recently, real-time control of divertor detachment has been successfully achieved in Type I ELMy H-mode JET-ITER-like wall discharges by using saturation current (I sat) measurements from divertor Langmuir probes as feedback signals to control the level of N seeding. The degree of divertor detachment is calculated in real-time by comparing the outer target peak I sat measurements to the peak I sat value at the roll-over in order to control the opening of the N injection valve. Real-time control of detachment has been achieved in both fixed and swept strike point experiments. The system has been progressively improved and can now automatically drive the divertor conditions from attached through high recycling and roll-over down to a user-defined level of detachment. Such a demonstration is a successful proof of principle in the context of future operation on ITER which will be extensively equipped with divertor target probes.

  15. AMYGDALA MICROCIRCUITS CONTROLLING LEARNED FEAR

    PubMed Central

    Duvarci, Sevil; Pare, Denis

    2014-01-01

    We review recent work on the role of intrinsic amygdala networks in the regulation of classically conditioned defensive behaviors, commonly known as conditioned fear. These new developments highlight how conditioned fear depends on far more complex networks than initially envisioned. Indeed, multiple parallel inhibitory and excitatory circuits are differentially recruited during the expression versus extinction of conditioned fear. Moreover, shifts between expression and extinction circuits involve coordinated interactions with different regions of the medial prefrontal cortex. However, key areas of uncertainty remain, particularly with respect to the connectivity of the different cell types. Filling these gaps in our knowledge is important because much evidence indicates that human anxiety disorders results from an abnormal regulation of the networks supporting fear learning. PMID:24908482

  16. Filtering sensory information with XCSF: improving learning robustness and robot arm control performance.

    PubMed

    Kneissler, Jan; Stalph, Patrick O; Drugowitsch, Jan; Butz, Martin V

    2014-01-01

    It has been shown previously that the control of a robot arm can be efficiently learned using the XCSF learning classifier system, which is a nonlinear regression system based on evolutionary computation. So far, however, the predictive knowledge about how actual motor activity changes the state of the arm system has not been exploited. In this paper, we utilize the forward velocity kinematics knowledge of XCSF to alleviate the negative effect of noisy sensors for successful learning and control. We incorporate Kalman filtering for estimating successive arm positions, iteratively combining sensory readings with XCSF-based predictions of hand position changes over time. The filtered arm position is used to improve both trajectory planning and further learning of the forward velocity kinematics. We test the approach on a simulated kinematic robot arm model. The results show that the combination can improve learning and control performance significantly. However, it also shows that variance estimates of XCSF prediction may be underestimated, in which case self-delusional spiraling effects can hinder effective learning. Thus, we introduce a heuristic parameter, which can be motivated by theory, and which limits the influence of XCSF's predictions on its own further learning input. As a result, we obtain drastic improvements in noise tolerance, allowing the system to cope with more than 10 times higher noise levels.

  17. A Real-Time Data Acquisition and Processing Framework Based on FlexRIO FPGA and ITER Fast Plant System Controller

    NASA Astrophysics Data System (ADS)

    Yang, C.; Zheng, W.; Zhang, M.; Yuan, T.; Zhuang, G.; Pan, Y.

    2016-06-01

    Measurement and control of the plasma in real-time are critical for advanced Tokamak operation. It requires high speed real-time data acquisition and processing. ITER has designed the Fast Plant System Controllers (FPSC) for these purposes. At J-TEXT Tokamak, a real-time data acquisition and processing framework has been designed and implemented using standard ITER FPSC technologies. The main hardware components of this framework are an Industrial Personal Computer (IPC) with a real-time system and FlexRIO devices based on FPGA. With FlexRIO devices, data can be processed by FPGA in real-time before they are passed to the CPU. The software elements are based on a real-time framework which runs under Red Hat Enterprise Linux MRG-R and uses Experimental Physics and Industrial Control System (EPICS) for monitoring and configuring. That makes the framework accord with ITER FPSC standard technology. With this framework, any kind of data acquisition and processing FlexRIO FPGA program can be configured with a FPSC. An application using the framework has been implemented for the polarimeter-interferometer diagnostic system on J-TEXT. The application is able to extract phase-shift information from the intermediate frequency signal produced by the polarimeter-interferometer diagnostic system and calculate plasma density profile in real-time. Different algorithms implementations on the FlexRIO FPGA are compared in the paper.

  18. Model learning for robot control: a survey.

    PubMed

    Nguyen-Tuong, Duy; Peters, Jan

    2011-11-01

    Models are among the most essential tools in robotics, such as kinematics and dynamics models of the robot's own body and controllable external objects. It is widely believed that intelligent mammals also rely on internal models in order to generate their actions. However, while classical robotics relies on manually generated models that are based on human insights into physics, future autonomous, cognitive robots need to be able to automatically generate models that are based on information which is extracted from the data streams accessible to the robot. In this paper, we survey the progress in model learning with a strong focus on robot control on a kinematic as well as dynamical level. Here, a model describes essential information about the behavior of the environment and the influence of an agent on this environment. In the context of model-based learning control, we view the model from three different perspectives. First, we need to study the different possible model learning architectures for robotics. Second, we discuss what kind of problems these architecture and the domain of robotics imply for the applicable learning methods. From this discussion, we deduce future directions of real-time learning algorithms. Third, we show where these scenarios have been used successfully in several case studies.

  19. Quality Control & Design in Science Learning

    ERIC Educational Resources Information Center

    Sumrall, William J.; Schillinger, Don

    2003-01-01

    One area of science education that is, at times, neglected involves lessons on technological concepts of these principles--designing, testing, and quality control. Instead, a focus upon science concepts from a pure, and unapplied, perspective is the norm. Thus, while students may learn the equation "mass divided by volume equals…

  20. Learned States of Preparatory Attentional Control

    ERIC Educational Resources Information Center

    Sali, Anthony W.; Anderson, Brian A.; Yantis, Steven

    2015-01-01

    Individuals regularly experience fluctuations in the ability to perform cognitive operations. Although previous research has focused on predicting cognitive flexibility from persistent individual traits, as well as from spontaneous fluctuations in neural activity, the role of learning in shaping preparatory attentional control remains poorly…

  1. Learned States of Preparatory Attentional Control

    ERIC Educational Resources Information Center

    Sali, Anthony W.; Anderson, Brian A.; Yantis, Steven

    2015-01-01

    Individuals regularly experience fluctuations in the ability to perform cognitive operations. Although previous research has focused on predicting cognitive flexibility from persistent individual traits, as well as from spontaneous fluctuations in neural activity, the role of learning in shaping preparatory attentional control remains poorly…

  2. Reinforcement learning output feedback NN control using deterministic learning technique.

    PubMed

    Xu, Bin; Yang, Chenguang; Shi, Zhongke

    2014-03-01

    In this brief, a novel adaptive-critic-based neural network (NN) controller is investigated for nonlinear pure-feedback systems. The controller design is based on the transformed predictor form, and the actor-critic NN control architecture includes two NNs, whereas the critic NN is used to approximate the strategic utility function, and the action NN is employed to minimize both the strategic utility function and the tracking error. A deterministic learning technique has been employed to guarantee that the partial persistent excitation condition of internal states is satisfied during tracking control to a periodic reference orbit. The uniformly ultimate boundedness of closed-loop signals is shown via Lyapunov stability analysis. Simulation results are presented to demonstrate the effectiveness of the proposed control.

  3. Modularity for Motor Control and Motor Learning.

    PubMed

    d'Avella, Andrea

    2016-01-01

    How the central nervous system (CNS) overcomes the complexity of multi-joint and multi-muscle control and how it acquires or adapts motor skills are fundamental and open questions in neuroscience. A modular architecture may simplify control by embedding features of both the dynamic behavior of the musculoskeletal system and of the task into a small number of modules and by directly mapping task goals into module combination parameters. Several studies of the electromyographic (EMG) activity recorded from many muscles during the performance of different tasks have shown that motor commands are generated by the combination of a small number of muscle synergies, coordinated recruitment of groups of muscles with specific amplitude balances or activation waveforms, thus supporting a modular organization of motor control. Modularity may also help understanding motor learning. In a modular architecture, acquisition of a new motor skill or adaptation of an existing skill after a perturbation may occur at the level of modules or at the level of module combinations. As learning or adapting an existing skill through recombination of modules is likely faster than learning or adapting a skill by acquiring new modules, compatibility with the modules predicts learning difficulty. A recent study in which human subjects used myoelectric control to move a mass in a virtual environment has tested this prediction. By altering the mapping between recorded muscle activity and simulated force applied on the mass, as in a complex surgical rearrangement of the tendons, it has been possible to show that it is easier to adapt to a perturbation that is compatible with the muscle synergies used to generate hand force than to a similar but incompatible perturbation. This result provides direct support for a modular organization of motor control and motor learning.

  4. Connectionist reinforcement learning of robot control skills

    NASA Astrophysics Data System (ADS)

    Araújo, Rui; Nunes, Urbano; de Almeida, A. T.

    1998-07-01

    Many robot manipulator tasks are difficult to model explicitly and it is difficult to design and program automatic control algorithms for them. The development, improvement, and application of learning techniques taking advantage of sensory information would enable the acquisition of new robot skills and avoid some of the difficulties of explicit programming. In this paper we use a reinforcement learning approach for on-line generation of skills for control of robot manipulator systems. Instead of generating skills by explicit programming of a perception to action mapping they are generated by trial and error learning, guided by a performance evaluation feedback function. The resulting system may be seen as an anticipatory system that constructs an internal representation model of itself and of its environment. This enables it to identify its current situation and to generate corresponding appropriate commands to the system in order to perform the required skill. The method was applied to the problem of learning a force control skill in which the tool-tip of a robot manipulator must be moved from a free space situation, to a contact state with a compliant surface and having a constant interaction force.

  5. The Capacity Building programmes of GITEWS - visions, goals, lessons learned, and re-iterated needs and demands

    NASA Astrophysics Data System (ADS)

    Schlurmann, T.; Siebert, M.

    2011-02-01

    hazards is still pending. Local authorities and researchers in tentative affected regions are now trained and enabled to disseminate and apply their knowledge and planning experience to other coastal regions in the area to help facilitating and multiplying effective disaster management plans and strategies. Yet, the Capacity Building framework within GITEWS also elucidated gaps in the early warning chain so that updated and to some extent re-iterated needs and demands in Capacity Building programs in any future research or development cooperation project are presented and discussed.

  6. Optimal chaos control through reinforcement learning.

    PubMed

    Gadaleta, Sabino; Dangelmayr, Gerhard

    1999-09-01

    A general purpose chaos control algorithm based on reinforcement learning is introduced and applied to the stabilization of unstable periodic orbits in various chaotic systems and to the targeting problem. The algorithm does not require any information about the dynamical system nor about the location of periodic orbits. Numerical tests demonstrate good and fast performance under noisy and nonstationary conditions. (c) 1999 American Institute of Physics.

  7. A Tale of Two Chambers: Iterative Approaches and Lessons Learned from Life Support Systems Testing in Altitude Chambers

    NASA Technical Reports Server (NTRS)

    Callini, Gianluca

    2016-01-01

    With a brand new fire set ablaze by a serendipitous convergence of events ranging from a science fiction novel and movie ("The Martian"), to ground-breaking recent discoveries of flowing water on its surface, the drive for the journey to Mars seems to be in a higher gear than ever before. We are developing new spacecraft and support systems to take humans to the Red Planet, while scientists on Earth continue using the International Space Station as a laboratory to evaluate the effects of long duration space flight on the human body. Written from the perspective of a facility test director rather than a researcher, and using past and current life support systems tests as examples, this paper seeks to provide an overview on how facility teams approach testing, the kind of information they need to ensure efficient collaborations and successful tests, and how, together with researchers and principal investigators, we can collectively apply what we learn to execute future tests.

  8. Consequences of deuterium retention and release from Be-containing mixed materials for ITER Tritium Inventory Control

    NASA Astrophysics Data System (ADS)

    Sugiyama, K.; Roth, J.; Anghel, A.; Porosnicu, C.; Baldwin, M.; Doerner, R.; Krieger, K.; Lungu, C. P.

    2011-08-01

    In order to assess the tritium removal procedure currently suggested for ITER (wall baking at 513 K (240 °C) for the main chamber, at 623 K (350 °C) for the divertor), deuterium (D) retention and release behaviour of beryllium (Be)-containing materials are investigated. In pure Be, D is predominantly released around 470 K with a relatively sharp desorption peak. Mixing of tungsten (W) or carbon (C) into Be changes the D desorption behaviour causing less efficiency D removal by the baking procedure. Especially, high C concentrations in Be affect the D release behaviour significantly and prevents removal of the retained D by baking at 623 K. As a consequence, the baking operation in ITER would work for tritium removal from the first wall and Be-rich deposited layers formed at low temperature areas, while it does not work for C-rich codeposited layers and/or plasma-facing surfaces heated above 623 K during a discharge.

  9. X-29 flight control system: Lessons learned

    NASA Technical Reports Server (NTRS)

    Clarke, Robert; Burken, John J.; Bosworth, John T.; Bauer, Jeffery E.

    1994-01-01

    Two X-29A aircraft were flown at the NASA Dryden Flight Research Center over a period of eight years. The airplanes' unique features are the forward-swept wing, variable incidence close-coupled canard and highly relaxed longitudinal static stability (up to 35-percent negative static margin at subsonic conditions). This paper describes the primary flight control system and significant modifications made to this system, flight test techniques used during envelope expansion, and results for the low- and high-angle-of-attack programs. Through out the paper, lessons learned will be discussed to illustrate the problems associated with the implementation of complex flight control systems.

  10. WE-G-18A-04: 3D Dictionary Learning Based Statistical Iterative Reconstruction for Low-Dose Cone Beam CT Imaging

    SciTech Connect

    Bai, T; Yan, H; Shi, F; Jia, X; Jiang, Steve B.; Lou, Y; Xu, Q; Mou, X

    2014-06-15

    Purpose: To develop a 3D dictionary learning based statistical reconstruction algorithm on graphic processing units (GPU), to improve the quality of low-dose cone beam CT (CBCT) imaging with high efficiency. Methods: A 3D dictionary containing 256 small volumes (atoms) of 3x3x3 voxels was trained from a high quality volume image. During reconstruction, we utilized a Cholesky decomposition based orthogonal matching pursuit algorithm to find a sparse representation on this dictionary basis of each patch in the reconstructed image, in order to regularize the image quality. To accelerate the time-consuming sparse coding in the 3D case, we implemented our algorithm in a parallel fashion by taking advantage of the tremendous computational power of GPU. Evaluations are performed based on a head-neck patient case. FDK reconstruction with full dataset of 364 projections is used as the reference. We compared the proposed 3D dictionary learning based method with a tight frame (TF) based one using a subset data of 121 projections. The image qualities under different resolutions in z-direction, with or without statistical weighting are also studied. Results: Compared to the TF-based CBCT reconstruction, our experiments indicated that 3D dictionary learning based CBCT reconstruction is able to recover finer structures, to remove more streaking artifacts, and is less susceptible to blocky artifacts. It is also observed that statistical reconstruction approach is sensitive to inconsistency between the forward and backward projection operations in parallel computing. Using high a spatial resolution along z direction helps improving the algorithm robustness. Conclusion: 3D dictionary learning based CBCT reconstruction algorithm is able to sense the structural information while suppressing noise, and hence to achieve high quality reconstruction. The GPU realization of the whole algorithm offers a significant efficiency enhancement, making this algorithm more feasible for potential

  11. Learning and control of exploration primitives.

    PubMed

    Gordon, Goren; Fonio, Ehud; Ahissar, Ehud

    2014-10-01

    Animals explore novel environments in a cautious manner, exhibiting alternation between curiosity-driven behavior and retreats. We present a detailed formal framework for exploration behavior, which generates behavior that maintains a constant level of novelty. Similar to other types of complex behaviors, the resulting exploratory behavior is composed of exploration motor primitives. These primitives can be learned during a developmental period, wherein the agent experiences repeated interactions with environments that share common traits, thus allowing transference of motor learning to novel environments. The emergence of exploration motor primitives is the result of reinforcement learning in which information gain serves as intrinsic reward. Furthermore, actors and critics are local and ego-centric, thus enabling transference to other environments. Novelty control, i.e. the principle which governs the maintenance of constant novelty, is implemented by a central action-selection mechanism, which switches between the emergent exploration primitives and a retreat policy, based on the currently-experienced novelty. The framework has only a few parameters, wherein time-scales, learning rates and thresholds are adaptive, and can thus be easily applied to many scenarios. We implement it by modeling the rodent's whisking system and show that it can explain characteristic observed behaviors. A detailed discussion of the framework's merits and flaws, as compared to other related models, concludes the paper.

  12. US ITER Moving Forward

    ScienceCinema

    US ITER / ORNL

    2016-07-12

    US ITER Project Manager Ned Sauthoff, joined by Wayne Reiersen, Team Leader Magnet Systems, and Jan Berry, Team Leader Tokamak Cooling System, discuss the U.S.'s role in the ITER international collaboration.

  13. Operating ITER Robustly Without Disruptions

    NASA Astrophysics Data System (ADS)

    Humphreys, D. A.; Eidietis, N. W.; Hyatt, A. W.; Leuer, J. A.; Luce, T. C.; Strait, E. J.; Walker, M. L.; Welander, A. S.; Wesley, J. C.; Lodestro, L.; Pearlstein, L. D.

    2011-10-01

    Disruptivity in ITER must be minimized to limit downtime and maximize use of the limited number of discharges. Minimizing disruptivity requires sufficient control capability, including robustness to disturbances and disruption avoidance through prediction of controllability limits. Robust control implies a balance of passively stable nominal scenarios, robust operation near or beyond open loop stability limits, and responses to off-normal events to avoid disruptive termination. Such a solution is possible because disruptions result from deterministic loss of controllability due to many proximal causes (e.g. loss of hardware resources, human error, or uncontrollable disturbances), most of which can be addressed with good physics models and known control methods. We illustrate the required approach with DIII-D experiments to assess ITER controllability and pre-qualify ITER scenarios, and with design and analysis ensuring sufficiently robust vertical control for ITER. Supported by the US DOE under DE-FC02-04ER54698 and DE-AC52-07NA27344.

  14. Iter and Ornl

    NASA Astrophysics Data System (ADS)

    Uckan, N. A.; Milora, S. L.

    2004-11-01

    ITER (means ``the way''), a tokamak burning plasma experiment, is the next step device toward making fusion energy a reality. The programmatic objective of ITER is to demonstrate the scientific and technological feasibility of fusion energy for peaceful purposes. ITER began in 1985 as collaboration between the Russian Federation (former Soviet Union), the USA, European Union, and Japan. ITER conceptual and engineering design activities led to a detailed design in 2001. The USA opted out of the project between 1999-2003, but rejoined in 2004 for site selection and construction negotiations. China and Korea joined the project in 2003. Negotiations are continuing and a decision on the site for ITER construction [France versus Japan] is pending. The ITER international undertaking is an unprecedented scale and the six ITER parties represent 40% of the world population. By 2018, ITER will produce a fusion power of 500 million Watts for time periods up to an hour with one-tenth of the power needed to sustain it. Steady state operation is also possible at lower power levels with higher fraction of circulated power. The ITER parties invested about $1 billion into the research and development (R) and related fusion experiments to establish the ITER's feasibility. ORNL has been a key player in the ITER project and contributed to its physics and engineering design and related R since its inception. Recently, the U.S. DOE selected the PPPL/ORNL partnership to lead the U.S. project office for ITER.

  15. Self-learning active noise control.

    PubMed

    Yuan, J

    2008-10-01

    An important step for active noise control (ANC) systems to be practical is to develop model independent ANC (MIANC) systems that tolerate parameter variations in sound fields. Reliabilities and stabilities of many MIANC systems depend on results of online system identifications. Parameter errors due to system identifications may threaten closed-loop stabilities of MIANC systems. A self-learning active noise control (SLANC) system is proposed in this study to stabilize and optimize an ANC system in case identified parameters are unreliable. The proposed system uses an objective function to check closed-loop stability. If partial or full value of the objective function exceeds a conservatively preset threshold, a stability threat is detected and the SLANC system will stabilize and optimize the controller without using parameters of sound fields. If the reference signal is available, the SLANC system can be combined with a feedforward controller to generate both destructive interference and active damping in sound fields. The self-learning method is simple and stable for many feedback ANC systems to deal with a worst case discussed in this study.

  16. Heritability of motor control and motor learning

    PubMed Central

    Missitzi, Julia; Gentner, Reinhard; Misitzi, Angelica; Geladas, Nickos; Politis, Panagiotis; Klissouras, Vassilis; Classen, Joseph

    2013-01-01

    Abstract The aim of this study was to elucidate the relative contribution of genes and environment on individual differences in motor control and acquisition of a force control task, in view of recent association studies showing that several candidate polymorphisms may have an effect on them. Forty‐four healthy female twins performed brisk isometric abductions with their right thumb. Force was recorded by a transducer and fed back to the subject on a computer screen. The task was to place the tracing of the peak force in a force window defined between 30% and 40% of the subject's maximum force, as determined beforehand. The initial level of proficiency was defined as the number of attempts reaching the force window criterion within the first 100 trials. The difference between the number of successful trials within the last and the first 100 trials was taken as a measure of motor learning. For motor control, defined by the initial level of proficiency, the intrapair differences in monozygotic (MZ) and dizygotic (DZ) twins were 6.8 ± 7.8 and 13.8 ± 8.4, and the intrapair correlations 0.77 and 0.39, respectively. Heritability was estimated at 0.68. Likewise for motor learning intrapair differences in the increment of the number of successful trials in MZ and DZ twins were 5.4 ± 5.2 and 12.8 ± 7, and the intrapair correlations 0.58 and 0.19. Heritability reached 0.70. The present findings suggest that heredity accounts for a major part of existing differences in motor control and motor learning, but uncertainty remains which gene polymorphisms may be responsible. PMID:24744865

  17. ITER safety challenges and opportunities

    SciTech Connect

    Piet, S.J.

    1991-01-01

    Results of the Conceptual Design Activity (CDA) for the International Thermonuclear Experimental Reactor (ITER) suggest challenges and opportunities. ITER is capable of meeting anticipated regulatory dose limits,'' but proof is difficult because of large radioactive inventories needing stringent radioactivity confinement. We need much research and development (R D) and design analysis to establish that ITER meets regulatory requirements. We have a further opportunity to do more to prove more of fusion's potential safety and environmental advantages and maximize the amount of ITER technology on the path toward fusion power plants. To fulfill these tasks, we need to overcome three programmatic challenges and three technical challenges. The first programmatic challenge is to fund a comprehensive safety and environmental ITER R D plan. Second is to strengthen safety and environment work and personnel in the international team. Third is to establish an external consultant group to advise the ITER Joint Team on designing ITER to meet safety requirements for siting by any of the Parties. The first of the three key technical challenges is plasma engineering -- burn control, plasma shutdown, disruptions, tritium burn fraction, and steady state operation. The second is the divertor, including tritium inventory, activation hazards, chemical reactions, and coolant disturbances. The third technical challenge is optimization of design requirements considering safety risk, technical risk, and cost. Some design requirements are now too strict; some are too lax. Fuel cycle design requirements are presently too strict, mandating inappropriate T separation from H and D. Heat sink requirements are presently too lax; they should be strengthened to ensure that maximum loss of coolant accident temperatures drop.

  18. Space Station Control Moment Gyroscope Lessons Learned

    NASA Technical Reports Server (NTRS)

    Gurrisi, Charles; Seidel, Raymond; Dickerson, Scott; Didziulis, Stephen; Frantz, Peter; Ferguson, Kevin

    2010-01-01

    Four 4760 Nms (3510 ft-lbf-s) Double Gimbal Control Moment Gyroscopes (DGCMG) with unlimited gimbal freedom about each axis were adopted by the International Space Station (ISS) Program as the non-propulsive solution for continuous attitude control. These CMGs with a life expectancy of approximately 10 years contain a flywheel spinning at 691 rad/s (6600 rpm) and can produce an output torque of 258 Nm (190 ft-lbf)1. One CMG unexpectedly failed after approximately 1.3 years and one developed anomalous behavior after approximately six years. Both units were returned to earth for failure investigation. This paper describes the Space Station Double Gimbal Control Moment Gyroscope design, on-orbit telemetry signatures and a summary of the results of both failure investigations. The lessons learned from these combined sources have lead to improvements in the design that will provide CMGs with greater reliability to assure the success of the Space Station. These lessons learned and design improvements are not only applicable to CMGs but can be applied to spacecraft mechanisms in general.

  19. Learning Dynamic Control of Body Roll Orientation

    PubMed Central

    Vimal, Vivekanand Pandey; Lackner, James R.; DiZio, Paul

    2016-01-01

    Our objective was to examine how the control of orientation is learned in a task involving dynamically balancing about an unstable equilibrium point, the gravitational vertical, in the absence of leg reflexes and muscle stiffness. Subjects (n=10) used a joystick to set themselves to the gravitational vertical while seated in a multi-axis rotation system device (MARS) programmed with inverted pendulum dynamics. The MARS is driven by powerful servomotors and can faithfully follow joystick commands up to 2.5 Hz with a 30 ms latency. To make the task extremely difficult, the pendulum constant was set to 600°/sec2. Each subject participated in 5 blocks of 4 trials, with a trial ending after a cumulative 100 s of balancing, excluding reset times when a subject lost control. To characterize performance and learning, we used metrics derived from joystick movements, phase portraits (joystick deflections vs MARS position and MARS velocity vs angular position), and stabilogram diffusion functions. We found that as subjects improved their balancing performance they did so by making fewer destabilizing joystick movements and reducing the number and duration of joystick commands. The control strategy they acquired involved making more persistent short-term joystick movements, waiting longer before making changes to ongoing motion, and only intervening intermittently. PMID:26525709

  20. Tunnel Ventilation Control Using Reinforcement Learning Methodology

    NASA Astrophysics Data System (ADS)

    Chu, Baeksuk; Kim, Dongnam; Hong, Daehie; Park, Jooyoung; Chung, Jin Taek; Kim, Tae-Hyung

    The main purpose of tunnel ventilation system is to maintain CO pollutant concentration and VI (visibility index) under an adequate level to provide drivers with comfortable and safe driving environment. Moreover, it is necessary to minimize power consumption used to operate ventilation system. To achieve the objectives, the control algorithm used in this research is reinforcement learning (RL) method. RL is a goal-directed learning of a mapping from situations to actions without relying on exemplary supervision or complete models of the environment. The goal of RL is to maximize a reward which is an evaluative feedback from the environment. In the process of constructing the reward of the tunnel ventilation system, two objectives listed above are included, that is, maintaining an adequate level of pollutants and minimizing power consumption. RL algorithm based on actor-critic architecture and gradient-following algorithm is adopted to the tunnel ventilation system. The simulations results performed with real data collected from existing tunnel ventilation system and real experimental verification are provided in this paper. It is confirmed that with the suggested controller, the pollutant level inside the tunnel was well maintained under allowable limit and the performance of energy consumption was improved compared to conventional control scheme.

  1. Learning dynamic control of body roll orientation.

    PubMed

    Vimal, Vivekanand Pandey; Lackner, James R; DiZio, Paul

    2016-02-01

    Our objective was to examine how the control of orientation is learned in a task involving dynamically balancing about an unstable equilibrium point, the gravitational vertical, in the absence of leg reflexes and muscle stiffness. Subjects (n = 10) used a joystick to set themselves to the gravitational vertical while seated in a multi-axis rotation system (MARS) device programmed with inverted pendulum dynamics. The MARS is driven by powerful servomotors and can faithfully follow joystick commands up to 2.5 Hz with a 30-ms latency. To make the task extremely difficult, the pendulum constant was set to 600°/s(2). Each subject participated in five blocks of four trials, with a trial ending after a cumulative 100 s of balancing, excluding reset times when a subject lost control. To characterize performance and learning, we used metrics derived from joystick movements, phase portraits (joystick deflections vs MARS position and MARS velocity vs angular position), and stabilogram diffusion functions. We found that as subjects improved their balancing performance, they did so by making fewer destabilizing joystick movements and reducing the number and duration of joystick commands. The control strategy they acquired involved making more persistent short-term joystick movements, waiting longer before making changes to ongoing motion, and only intervening intermittently.

  2. Iterated fractional Tikhonov regularization

    NASA Astrophysics Data System (ADS)

    Bianchi, Davide; Buccini, Alessandro; Donatelli, Marco; Serra-Capizzano, Stefano

    2015-05-01

    Fractional Tikhonov regularization methods have been recently proposed to reduce the oversmoothing property of the Tikhonov regularization in standard form, in order to preserve the details of the approximated solution. Their regularization and convergence properties have been previously investigated showing that they are of optimal order. This paper provides saturation and converse results on their convergence rates. Using the same iterative refinement strategy of iterated Tikhonov regularization, new iterated fractional Tikhonov regularization methods are introduced. We show that these iterated methods are of optimal order and overcome the previous saturation results. Furthermore, nonstationary iterated fractional Tikhonov regularization methods are investigated, establishing their convergence rate under general conditions on the iteration parameters. Numerical results confirm the effectiveness of the proposed regularization iterations.

  3. Microtearing instability in ITER*

    NASA Astrophysics Data System (ADS)

    Wong, King-Lap; Mikkelsen, David; Budny, Robert; Breslau, Joshua

    2010-11-01

    Microtearing modes are found to be unstable in some regions of a simulated ITER H-mode plasma [1] with the GS2 code [2]. Modes with kρs>1 are in the interior (r/a˜0.65-0.85) while longer wavelength modes are in the pedestal region. This instability may keep the pedestal within the peeling-ballooning stability boundary [3]. Microtearing modes can produce stochastic magnetic field similar to RMP coils; they may have similar effects on ELMs by increasing the pedestal width. The possibility of using this technique for ELM mitigation in ITER is explored. We propose to use a deuterium gas jet to control the microtearing instability and the Chirikov parameter at the edge. Preliminary evaluation of its effectiveness will be presented and the limitations of the GS2 code will be discussed based on our understanding from NSTX [4]. *This work is supported by USDoE contract DE-AC02-09CH11466. [4pt] [1] R. V. Budny, Nucl. Fusion (2009)[0pt] [2] W. Dorland et al., Phys. Rev. Lett. (2000).[0pt] [3] P. B. Snyder et al.,Nucl. Fusion (2009).[0pt] [4] K. L. Wong et al., Phys. Rev. Lett. (2007).

  4. An Iterative Solution to the Nonlinear Time-Discrete TEM Model - The Occurrence of Chaos and a Control Theoretic Algorithmic Approach

    NASA Astrophysics Data System (ADS)

    Pickl, S.

    2002-09-01

    This paper is concerned with a mathematical derivation of the nonlinear time-discrete Technology-Emissions Means (TEM-) model. A detailed introduction to the dynamics modelling a Joint Implementation Program concerning Kyoto Protocol is given at the end of the paper. As the nonlinear time-discrete dynamics tends to chaotic behaviour, the necessary introduction of control parameters in the dynamics of the TEM model leads to new results in the field of time-discrete control systems. Furthermore the numerical results give new insights into a Joint-Implementation Program and herewith, they may improve this important economic tool. The iterative solution presented at the end might be a useful orientation to anticipate and support Kyoto Process.

  5. Language learning without control: the role of the PFC.

    PubMed

    Friederici, Angela D; Mueller, Jutta L; Sehm, Bernhard; Ragert, Patrick

    2013-05-01

    Learning takes place throughout lifetime but differs in infants and adults because of the development of the PFC, a brain region responsible for cognitive control. To test this hypothesis, adults were investigated in a language learning paradigm under inhibitory, cathodal transcranial direct current stimulation over PFC. The experiment included a learning session interspersed with test phases and a test-only session. The stimulus material required the learning of grammatical dependencies between two elements in a novel language. In a parallel design, cathodal transcranial direct current stimulation over the left PFC, right PFC, or sham stimulation was applied during the learning session but not during the test-only session. Event-related brain potentials (ERPs) were recorded during both sessions. Whereas no ERP learning effects were observed during the learning session, different ERP learning effects as a function of prior stimulation type were found during the test-only session, although behavioral learning success was equal across conditions. With sham stimulation, the ERP learning effect was reflected in a centro-parietal N400-like negativity indicating lexical processes. Inhibitory stimulation over the left PFC, but not over the right PFC, led to a late positivity similar to that previously observed in prelinguistic infants indicating associative learning. The present data demonstrate that adults can learn with and without cognitive control using different learning mechanisms. In the presence of cognitive control, adult language learning is lexically guided, whereas it appears to be associative in nature when PFC control is downregulated.

  6. The real mission of ITER

    SciTech Connect

    Wurden, G A

    2009-01-01

    For future machines, the plasma stored energy is going up by factors of 20-40x, and plasma currents by 2-3x, while the surface to volume ratio is at the same time decreasing. Therefore the disruption forces, even for constant B, (which scale like IxB), and associated possible localized heating on machine components, are more severe. Notably, Tore Supra has demonstrated removal of more than 1 GJ of input energy, over nearly a 400 second period. However, the instantaneous stored energy in the Tore Supra system (which is most directly related to the potential for disruption damage) is quite small compared to other large tokamaks. The goal of ITER is routinely described as studying DT burning plasmas with a Q {approx} 10. In reality, ITER has a much more important first order mission. In fact, if it fails at this mission, the consequences are that ITER will never get to the eventual stated purpose of studying a burning plasma. The real mission of ITER is to study (and demonstrate successfully) plasma control with {approx}10-17 MA toroidal currents and {approx}100-400 MJ plasma stored energy levels in long-pulse scenarios. Before DT operation is ever given a go-ahead in ITER, the reality is that ITER must demonstrate routine and reliable control of high energy hydrogen (and deuterium) plasmas. The difficulty is that ITER must simultaneously deal with several technical problems: (1) heat removal at the plasma/wall interface, (2) protection of the wall components from off-normal events, and (3) generation of dust/redeposition of first wall materials. All previous tokamaks have encountered hundred's of major disruptions in the course of their operation. The consequences of a few MA of runaway electrons (at 20-50 MeV) being generated in ITER, and then being lost to the walls are simply catastrophic. They will not be deposited globally, but will drift out (up, down, whatever, depending on control system), and impact internal structures, unless 'ameliorated'. Basically, this

  7. Online Learning Flight Control for Intelligent Flight Control Systems (IFCS)

    NASA Technical Reports Server (NTRS)

    Niewoehner, Kevin R.; Carter, John (Technical Monitor)

    2001-01-01

    The research accomplishments for the cooperative agreement 'Online Learning Flight Control for Intelligent Flight Control Systems (IFCS)' include the following: (1) previous IFC program data collection and analysis; (2) IFC program support site (configured IFC systems support network, configured Tornado/VxWorks OS development system, made Configuration and Documentation Management Systems Internet accessible); (3) Airborne Research Test Systems (ARTS) II Hardware (developed hardware requirements specification, developing environmental testing requirements, hardware design, and hardware design development); (4) ARTS II software development laboratory unit (procurement of lab style hardware, configured lab style hardware, and designed interface module equivalent to ARTS II faceplate); (5) program support documentation (developed software development plan, configuration management plan, and software verification and validation plan); (6) LWR algorithm analysis (performed timing and profiling on algorithm); (7) pre-trained neural network analysis; (8) Dynamic Cell Structures (DCS) Neural Network Analysis (performing timing and profiling on algorithm); and (9) conducted technical interchange and quarterly meetings to define IFC research goals.

  8. Short wavelength interferometer for ITER

    SciTech Connect

    Snider, R.T.; Carlstrom, T.N.

    1992-04-01

    There is a need for a real time, reliable density measurement compatible with the restricted access and radiation environment on ITER. Due to the large plasma path length, high density and field, refraction and Faraday rotation effects makes the use of contemporary long wavelength (>50{mu}m) interferometers impractical. In this paper we consider the design of a short wavelength vibration compensated interferometer which allows operation without a prohibitively large vibration isolated structure and permits the optics to be conveniently mounted directly in or on the tokamak. A density interferometer design for ITER incorporating a 10.6 {mu}m CO{sub 2} interferometer with vibration compensation provided by a 3. 39 {mu}m HeNe laser is discussed. The proposed interferometer design requires only a small intrusion into the ITER tokamak without a large support structure, refraction and Faraday rotation problems are avoided, and it provides a density resolution of at least 0.5%. Results are presented from an interferometer installed on the DIII-D tokamak incorporating essential elements of the proposed ITER design including 10.6 and 3.39 {mu}m lasers, a retro-reflector mounted on the vacuum wall of the DIII-D tokamak and real-time density feedback control. In this paper we consider a short wavelength interferometer design that incorporates vibration compensation for use on ITER. Our primary concern is to develop a interferometer design that will produce a reliable real time density monitor. We use the ITER conceptual design activity report as the basis of the design.

  9. Energetic ions in ITER plasmas

    SciTech Connect

    Pinches, S. D.; Chapman, I. T.; Sharapov, S. E.; Lauber, Ph. W.; Oliver, H. J. C.; Shinohara, K.; Tani, K.

    2015-02-15

    This paper discusses the behaviour and consequences of the expected populations of energetic ions in ITER plasmas. It begins with a careful analytic and numerical consideration of the stability of Alfvén Eigenmodes in the ITER 15 MA baseline scenario. The stability threshold is determined by balancing the energetic ion drive against the dominant damping mechanisms and it is found that only in the outer half of the plasma (r/a>0.5) can the fast ions overcome the thermal ion Landau damping. This is in spite of the reduced numbers of alpha-particles and beam ions in this region but means that any Alfvén Eigenmode-induced redistribution is not expected to influence the fusion burn process. The influence of energetic ions upon the main global MHD phenomena expected in ITER's primary operating scenarios, including sawteeth, neoclassical tearing modes and Resistive Wall Modes, is also reviewed. Fast ion losses due to the non-axisymmetric fields arising from the finite number of toroidal field coils, the inclusion of ferromagnetic inserts, the presence of test blanket modules containing ferromagnetic material, and the fields created by the Edge Localised Mode (ELM) control coils in ITER are discussed. The greatest losses and associated heat loads onto the plasma facing components arise due to the use of the ELM control coils and come from neutral beam ions that are ionised in the plasma edge.

  10. Intentional control and implicit sequence learning.

    PubMed

    Wilkinson, Leonora; Shanks, David R

    2004-03-01

    Sequence knowledge acquired by repeated exposure to targets in a speeded localization task was studied in 3 experiments that sought to test A. Destrebecqz and A. Cleeremans's (2001, 2003) claim that, under certain circumstances, the expression of such sequence knowledge cannot be brought under intentional control. In Experiment 1 participants were trained on either a deterministic or a probabilistic sequence and then performed a free-generation test under either inclusion or exclusion instructions. Participants were found to be capable of both expressing (inclusion) and avoiding expressing (exclusion) sequence knowledge. These results were confirmed in Experiment 2 with a more exact replication of Destrebecqz and Cleeremans's methodology. In Experiment 3 participants performed a trial-by-trial generation test under both inclusion and exclusion conditions after a much longer period of training. All the findings are consistent with the proposal that information acquired during sequence learning is explicit in nature.

  11. Exploring Learner Autonomy: Language Learning Locus of Control in Multilinguals

    ERIC Educational Resources Information Center

    Peek, Ron

    2016-01-01

    By using data from an online language learning beliefs survey (n?=?841), defining language learning experience in terms of participants' multilingualism, and using a domain-specific language learning locus of control (LLLOC) instrument, this article examines whether more experienced language learners can also be seen as more autonomous language…

  12. Exploring Learner Autonomy: Language Learning Locus of Control in Multilinguals

    ERIC Educational Resources Information Center

    Peek, Ron

    2016-01-01

    By using data from an online language learning beliefs survey (n?=?841), defining language learning experience in terms of participants' multilingualism, and using a domain-specific language learning locus of control (LLLOC) instrument, this article examines whether more experienced language learners can also be seen as more autonomous language…

  13. Adaptive Performance Seeking Control Using Fuzzy Model Reference Learning Control and Positive Gradient Control

    NASA Technical Reports Server (NTRS)

    Kopasakis, George

    1997-01-01

    Performance Seeking Control attempts to find the operating condition that will generate optimal performance and control the plant at that operating condition. In this paper a nonlinear multivariable Adaptive Performance Seeking Control (APSC) methodology will be developed and it will be demonstrated on a nonlinear system. The APSC is comprised of the Positive Gradient Control (PGC) and the Fuzzy Model Reference Learning Control (FMRLC). The PGC computes the positive gradients of the desired performance function with respect to the control inputs in order to drive the plant set points to the operating point that will produce optimal performance. The PGC approach will be derived in this paper. The feedback control of the plant is performed by the FMRLC. For the FMRLC, the conventional fuzzy model reference learning control methodology is utilized, with guidelines generated here for the effective tuning of the FMRLC controller.

  14. Performances of the fractal iterative method with an internal model control law on the ESO end-to-end ELT adaptive optics simulator

    NASA Astrophysics Data System (ADS)

    Béchet, C.; Le Louarn, M.; Tallon, M.; Thiébaut, É.

    2008-07-01

    Adaptive Optics systems under study for the Extremely Large Telescopes gave rise to a new generation of algorithms for both wavefront reconstruction and the control law. In the first place, the large number of controlled actuators impose the use of computationally efficient methods. Secondly, the performance criterion is no longer solely based on nulling residual measurements. Priors on turbulence must be inserted. In order to satisfy these two requirements, we suggested to associate the Fractal Iterative Method for the estimation step with an Internal Model Control. This combination has now been tested on an end-to-end adaptive optics numerical simulator at ESO, named Octopus. Results are presented here and performance of our method is compared to the classical Matrix-Vector Multiplication combined with a pure integrator. In the light of a theoretical analysis of our control algorithm, we investigate the influence of several errors contributions on our simulations. The reconstruction error varies with the signal-to-noise ratio but is limited by the use of priors. The ratio between the system loop delay and the wavefront coherence time also impacts on the reachable Strehl ratio. Whereas no instabilities are observed, correction quality is obviously affected at low flux, when subapertures extinctions are frequent. Last but not least, the simulations have demonstrated the robustness of the method with respect to sensor modeling errors and actuators misalignments.

  15. Iteration, Not Induction

    ERIC Educational Resources Information Center

    Dobbs, David E.

    2009-01-01

    The main purpose of this note is to present and justify proof via iteration as an intuitive, creative and empowering method that is often available and preferable as an alternative to proofs via either mathematical induction or the well-ordering principle. The method of iteration depends only on the fact that any strictly decreasing sequence of…

  16. Iteration, Not Induction

    ERIC Educational Resources Information Center

    Dobbs, David E.

    2009-01-01

    The main purpose of this note is to present and justify proof via iteration as an intuitive, creative and empowering method that is often available and preferable as an alternative to proofs via either mathematical induction or the well-ordering principle. The method of iteration depends only on the fact that any strictly decreasing sequence of…

  17. Active controllers and the time duration to learn a task

    NASA Technical Reports Server (NTRS)

    Repperger, D. W.; Goodyear, C.

    1986-01-01

    An active controller was used to help train naive subjects involved in a compensatory tracking task. The controller is called active in this context because it moves the subject's hand in a direction to improve tracking. It is of interest here to question whether the active controller helps the subject to learn a task more rapidly than the passive controller. Six subjects, inexperienced to compensatory tracking, were run to asymptote root mean square error tracking levels with an active controller or a passive controller. The time required to learn the task was defined several different ways. The results of the different measures of learning were examined across pools of subjects and across controllers using statistical tests. The comparison between the active controller and the passive controller as to their ability to accelerate the learning process as well as reduce levels of asymptotic tracking error is reported here.

  18. Neural networks for self-learning control systems

    NASA Technical Reports Server (NTRS)

    Nguyen, Derrick H.; Widrow, Bernard

    1990-01-01

    It is shown how a neural network can learn of its own accord to control a nonlinear dynamic system. An emulator, a multilayered neural network, learns to identify the system's dynamic characteristics. The controller, another multilayered neural network, next learns to control the emulator. The self-trained controller is then used to control the actual dynamic system. The learning process continues as the emulator and controller improve and track the physical process. An example is given to illustrate these ideas. The 'truck backer-upper,' a neural network controller that steers a trailer truck while the truck is backing up to a loading dock, is demonstrated. The controller is able to guide the truck to the dock from almost any initial position. The technique explored should be applicable to a wide variety of nonlinear control problems.

  19. Neural networks for self-learning control systems

    NASA Technical Reports Server (NTRS)

    Nguyen, Derrick H.; Widrow, Bernard

    1990-01-01

    It is shown how a neural network can learn of its own accord to control a nonlinear dynamic system. An emulator, a multilayered neural network, learns to identify the system's dynamic characteristics. The controller, another multilayered neural network, next learns to control the emulator. The self-trained controller is then used to control the actual dynamic system. The learning process continues as the emulator and controller improve and track the physical process. An example is given to illustrate these ideas. The 'truck backer-upper,' a neural network controller that steers a trailer truck while the truck is backing up to a loading dock, is demonstrated. The controller is able to guide the truck to the dock from almost any initial position. The technique explored should be applicable to a wide variety of nonlinear control problems.

  20. Adaptive vibration suppression system: an iterative control law for a piezoelectric actuator shunted by a negative capacitor.

    PubMed

    Kodejska, Milos; Mokry, Pavel; Linhart, Vaclav; Vaclavik, Jan; Sluka, Tomas

    2012-12-01

    An adaptive system for the suppression of vibration transmission using a single piezoelectric actuator shunted by a negative capacitance circuit is presented. It is known that by using a negative-capacitance shunt, the spring constant of a piezoelectric actuator can be controlled to extreme values of zero or infinity. Because the value of spring constant controls a force transmitted through an elastic element, it is possible to achieve a reduction of transmissibility of vibrations through the use of a piezoelectric actuator by reducing its effective spring constant. Narrow frequency range and broad frequency range vibration isolation systems are analyzed, modeled, and experimentally investigated. The problem of high sensitivity of the vibration control system to varying operational conditions is resolved by applying an adaptive control to the circuit parameters of the negative capacitor. A control law that is based on the estimation of the value of the effective spring constant of a shunted piezoelectric actuator is presented. An adaptive system which achieves a self-adjustment of the negative capacitor parameters is presented. It is shown that such an arrangement allows the design of a simple electronic system which offers a great vibration isolation efficiency under variable vibration conditions.

  1. Multimedia Learning Environments: Issues of Learner Control and Navigation.

    ERIC Educational Resources Information Center

    Lawless, Kimberly A.; Brown, Scott W.

    1997-01-01

    Examines how different internal learner characteristics (prior knowledge, self efficacy, and interest) and different external constraints (learner control, instructional design, and level of control) influence the learning process. Discusses learning from multimedia environments (video, hypertext, kiosk, and other hypermedia) within a schema…

  2. JET helps prepare for ITER operation

    NASA Astrophysics Data System (ADS)

    Watkins, Michael

    2005-10-01

    The main focus of the JET programme (2006-10) in preparation of ITER operation is a new ITER-like ICRH antenna (total RF power increased to ˜15MW), a new ITER-like first wall (beryllium in the main chamber, tungsten in the divertor, and possibly CFC at the strike points), upgraded NB power (to 35MW/20s or 17.5MW/10s), and an improved diagnostic and control capability. Mass flows for ITER Scenarios with the ITER-like first wall will be optimised, particularly to minimise in-vessel tritium inventory, since this must be controlled strictly in ITER and has been shown on JET with a carbon first wall to depend sensitively on plasma conditions. Higher power will allow confinement scalings to be resolved for normalised parameters closer to ITER (beta dependence of ELMy H-modes, confinement of improved H-modes at low ρ*) and offers the prospect of high beta operation at high current and density, and new fully non-inductive, high performance, ITB discharges sustained to long pulse by real time current and pressure profile control, particularly in bootstrap current dominated regimes. Together, the first wall and increased heating power place strict constraints on the optimisation of ITER scenarios for long pulse operation with low melt damage. Large ELMs (in excess of 1MJ; marginally accessible on JET at present) and disruptions could cause melt severe damage which must be studied and controlled. The testing and optimisation of techniques for ELM mitigation (impurity seeding, demonstrated on JET; use of a new high frequency pellet injector (10-60Hz) to prevent large ELMs, demonstrated on ASDEX Upgrade) and disruption mitigation (fast gas injection from a new disruption mitigation valve, demonstrated on DIII-D) will be even more relevant under the ITER-like edge plasma conditions accessible with the increased power. Acknowledgement : Contributors to EFDA-JET Workprogramme

  3. A reinforcement learning-based architecture for fuzzy logic control

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1992-01-01

    This paper introduces a new method for learning to refine a rule-based fuzzy logic controller. A reinforcement learning technique is used in conjunction with a multilayer neural network model of a fuzzy controller. The approximate reasoning based intelligent control (ARIC) architecture proposed here learns by updating its prediction of the physical system's behavior and fine tunes a control knowledge base. Its theory is related to Sutton's temporal difference (TD) method. Because ARIC has the advantage of using the control knowledge of an experienced operator and fine tuning it through the process of learning, it learns faster than systems that train networks from scratch. The approach is applied to a cart-pole balancing system.

  4. A reinforcement learning-based architecture for fuzzy logic control

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1992-01-01

    This paper introduces a new method for learning to refine a rule-based fuzzy logic controller. A reinforcement learning technique is used in conjunction with a multilayer neural network model of a fuzzy controller. The approximate reasoning based intelligent control (ARIC) architecture proposed here learns by updating its prediction of the physical system's behavior and fine tunes a control knowledge base. Its theory is related to Sutton's temporal difference (TD) method. Because ARIC has the advantage of using the control knowledge of an experienced operator and fine tuning it through the process of learning, it learns faster than systems that train networks from scratch. The approach is applied to a cart-pole balancing system.

  5. I-mode for ITER?

    NASA Astrophysics Data System (ADS)

    Whyte, D. G.; Marmar, E.; Hubbard, A.; Hughes, J.; Dominguez, A.; Greenwald, M.

    2011-10-01

    I-mode is a recently explored confinement regime that features a temperature pedestal and H-mode energy confinement, yet with L-mode particle confinement and no density pedestal nor large ELMs. Experiments on Alcator C-Mod and ASDEX-Upgrade show this leads to a stationary collisionless pedestal that inherently does not require ELMs for core impurity and particle control, possibly making I-mode an attractive operating regime for ITER where ELM heat pulses are expected to surpass material limits. We speculate as to how I-mode could be obtained, maintained and exploited for the ITER burning plasma physics mission. Issues examined include I-mode topology and power threshold requirements, pedestal formation, density control, avoiding H-mode, and the response of I-mode to alpha self-heating. Key uncertainties requiring further investigation are identified. Supported by the US DOE Cooperative Agreement DE-FC02-99ER54512.

  6. Communal learning within a distributed robotic control system

    NASA Astrophysics Data System (ADS)

    Digney, Bruce L.

    2001-09-01

    It is accepted that the ability to learn and adapt is key to prosperity and survival in both individuals and societies. The same is true of populations of robots. Those robots within a population that are able to learn will outperform, survive longer and perhaps exploit their non-learning co- workers. This paper describes the ongoing results of Communal Learning in the Cognitive Colonies Project (CMU/Robotics and DRES), funded jointly by DARPA ITO- Software for Distributed Robotics and DRDC-DRES. Discussed will be how communal learning fits into the free market architecture for distributed control. Techniques for representing experiences, learned behaviors, maps and computational resources as commodities within the market economy will be presented. Once in a commodity structure, the cycle of speculate, act, receive profits or sustain losses and then learn of the market economy. This allows successful control strategies to emerge and the individuals who discovered them to become established as successful. This paper will discuss: learning to predict costs and make better deals, learning transition confidences, learning causes of death, learning with robot sacrifice and learning model parameters.

  7. The ITER design

    NASA Astrophysics Data System (ADS)

    Aymar, R.; Barabaschi, P.; Shimomura, Y.

    2002-05-01

    In 1998, after six years of joint work originally foreseen under the ITER engineering design activities (EDA) agreement, a design for ITER had been developed fulfilling all objectives and the cost target adopted by the ITER parties in 1992 at the start of the EDA. While accepting this design, the ITER parties recognized the possibility that they might be unable, for financial reasons, to proceed to the construction of the then foreseen device. The focus of effort in the ITER EDA since 1998 has been the development of a new design to meet revised technical objectives and a cost reduction target of about 50% of the previously accepted cost estimate. The rationale for the choice of parameters of the design has been based largely on system analysis drawing on the design solutions already developed and using the latest physics results and outputs from technology R&D projects. In so doing the joint central team and home teams converge towards a new design which will allow the exploration of a range of burning plasma conditions. The new ITER design, whilst having reduced technical objectives from its predecessor, will nonetheless meet the programmatic objective of providing an integrated demonstration of the scientific and technological feasibility of fusion energy. Background, design features, performance, safety features, and R&D and future perspectives of the ITER design are discussed.

  8. Techniques for improving transients in learning control systems

    NASA Technical Reports Server (NTRS)

    Chang, C.-K.; Longman, Richard W.; Phan, Minh

    1992-01-01

    A discrete modern control formulation is used to study the nature of the transient behavior of the learning process during repetitions. Several alternative learning control schemes are developed to improve the transient performance. These include a new method using an alternating sign on the learning gain, which is very effective in limiting peak transients and also very useful in multiple-input, multiple-output systems. Other methods include learning at an increasing number of points progressing with time, or an increasing number of points of increasing density.

  9. Perl Modules for Constructing Iterators

    NASA Technical Reports Server (NTRS)

    Tilmes, Curt

    2009-01-01

    The Iterator Perl Module provides a general-purpose framework for constructing iterator objects within Perl, and a standard API for interacting with those objects. Iterators are an object-oriented design pattern where a description of a series of values is used in a constructor. Subsequent queries can request values in that series. These Perl modules build on the standard Iterator framework and provide iterators for some other types of values. Iterator::DateTime constructs iterators from DateTime objects or Date::Parse descriptions and ICal/RFC 2445 style re-currence descriptions. It supports a variety of input parameters, including a start to the sequence, an end to the sequence, an Ical/RFC 2445 recurrence describing the frequency of the values in the series, and a format description that can refine the presentation manner of the DateTime. Iterator::String constructs iterators from string representations. This module is useful in contexts where the API consists of supplying a string and getting back an iterator where the specific iteration desired is opaque to the caller. It is of particular value to the Iterator::Hash module which provides nested iterations. Iterator::Hash constructs iterators from Perl hashes that can include multiple iterators. The constructed iterators will return all the permutations of the iterations of the hash by nested iteration of embedded iterators. A hash simply includes a set of keys mapped to values. It is a very common data structure used throughout Perl programming. The Iterator:: Hash module allows a hash to include strings defining iterators (parsed and dispatched with Iterator::String) that are used to construct an overall series of hash values.

  10. ITER Cryoplant Infrastructures

    NASA Astrophysics Data System (ADS)

    Fauve, E.; Monneret, E.; Voigt, T.; Vincent, G.; Forgeas, A.; Simon, M.

    2017-02-01

    The ITER Tokamak requires an average 75 kW of refrigeration power at 4.5 K and 600 kW of refrigeration Power at 80 K to maintain the nominal operation condition of the ITER thermal shields, superconducting magnets and cryopumps. This is produced by the ITER Cryoplant, a complex cluster of refrigeration systems including in particular three identical Liquid Helium Plants and two identical Liquid Nitrogen Plants. Beyond the equipment directly part of the Cryoplant, colossal infrastructures are required. These infrastructures account for a large part of the Cryoplants lay-out, budget and engineering efforts. It is ITER Organization responsibility to ensure that all infrastructures are adequately sized and designed to interface with the Cryoplant. This proceeding presents the overall architecture of the cryoplant. It provides order of magnitude related to the cryoplant building and utilities: electricity, cooling water, heating, ventilation and air conditioning (HVAC).

  11. Diagnostics for ITER

    SciTech Connect

    Donne, A. J. H.; Hellermann, M. G. von; Barnsley, R.

    2008-10-22

    After an introduction into the specific challenges in the field of diagnostics for ITER (specifically high level of nuclear radiation, long pulses, high fluxes of particles to plasma facing components, need for reliability and robustness), an overview will be given of the spectroscopic diagnostics foreseen for ITER. The paper will describe both active neutral-beam based diagnostics as well as passive spectroscopic diagnostics operating in the visible, ultra-violet and x-ray spectral regions.

  12. Mode conversion in ITER

    NASA Astrophysics Data System (ADS)

    Jaeger, E. F.; Berry, L. A.; Myra, J. R.

    2006-10-01

    Fast magnetosonic waves in the ion cyclotron range of frequencies (ICRF) can convert to much shorter wavelength modes such as ion Bernstein waves (IBW) and ion cyclotron waves (ICW) [1]. These modes are potentially useful for plasma control through the generation of localized currents and sheared flows. As part of the SciDAC Center for Simulation of Wave-Plasma Interactions project, the AORSA global-wave solver [2] has been ported to the new, dual-core Cray XT-3 (Jaguar) at ORNL where it demonstrates excellent scaling with the number of processors. Preliminary calculations using 4096 processors have allowed the first full-wave simulations of mode conversion in ITER. Mode conversion from the fast wave to the ICW is observed in mixtures of deuterium, tritium and helium3 at 53 MHz. The resulting flow velocity and electric field shear will be calculated. [1] F.W. Perkins, Nucl. Fusion 17, 1197 (1977). [2] E.F. Jaeger, L.A. Berry, J.R. Myra, et al., Phys. Rev. Lett. 90, 195001-1 (2003).

  13. Efficient model learning methods for actor-critic control.

    PubMed

    Grondman, Ivo; Vaandrager, Maarten; Buşoniu, Lucian; Babuska, Robert; Schuitema, Erik

    2012-06-01

    We propose two new actor-critic algorithms for reinforcement learning. Both algorithms use local linear regression (LLR) to learn approximations of the functions involved. A crucial feature of the algorithms is that they also learn a process model, and this, in combination with LLR, provides an efficient policy update for faster learning. The first algorithm uses a novel model-based update rule for the actor parameters. The second algorithm does not use an explicit actor but learns a reference model which represents a desired behavior, from which desired control actions can be calculated using the inverse of the learned process model. The two novel methods and a standard actor-critic algorithm are applied to the pendulum swing-up problem, in which the novel methods achieve faster learning than the standard algorithm.

  14. Near-complete elimination of mutant mtDNA by iterative or dynamic dose-controlled treatment with mtZFNs

    PubMed Central

    Gammage, Payam A.; Gaude, Edoardo; Van Haute, Lindsey; Rebelo-Guiomar, Pedro; Jackson, Christopher B.; Rorbach, Joanna; Pekalski, Marcin L.; Robinson, Alan J.; Charpentier, Marine; Concordet, Jean-Paul; Frezza, Christian; Minczuk, Michal

    2016-01-01

    Mitochondrial diseases are frequently associated with mutations in mitochondrial DNA (mtDNA). In most cases, mutant and wild-type mtDNAs coexist, resulting in heteroplasmy. The selective elimination of mutant mtDNA, and consequent enrichment of wild-type mtDNA, can rescue pathological phenotypes in heteroplasmic cells. Use of the mitochondrially targeted zinc finger-nuclease (mtZFN) results in degradation of mutant mtDNA through site-specific DNA cleavage. Here, we describe a substantial enhancement of our previous mtZFN-based approaches to targeting mtDNA, allowing near-complete directional shifts of mtDNA heteroplasmy, either by iterative treatment or through finely controlled expression of mtZFN, which limits off-target catalysis and undesired mtDNA copy number depletion. To demonstrate the utility of this improved approach, we generated an isogenic distribution of heteroplasmic cells with variable mtDNA mutant level from the same parental source without clonal selection. Analysis of these populations demonstrated an altered metabolic signature in cells harbouring decreased levels of mutant m.8993T>G mtDNA, associated with neuropathy, ataxia, and retinitis pigmentosa (NARP). We conclude that mtZFN-based approaches offer means for mtDNA heteroplasmy manipulation in basic research, and may provide a strategy for therapeutic intervention in selected mitochondrial diseases. PMID:27466392

  15. Towards autonomous neuroprosthetic control using Hebbian reinforcement learning

    NASA Astrophysics Data System (ADS)

    Mahmoudi, Babak; Pohlmeyer, Eric A.; Prins, Noeline W.; Geng, Shijia; Sanchez, Justin C.

    2013-12-01

    Objective. Our goal was to design an adaptive neuroprosthetic controller that could learn the mapping from neural states to prosthetic actions and automatically adjust adaptation using only a binary evaluative feedback as a measure of desirability/undesirability of performance. Approach. Hebbian reinforcement learning (HRL) in a connectionist network was used for the design of the adaptive controller. The method combines the efficiency of supervised learning with the generality of reinforcement learning. The convergence properties of this approach were studied using both closed-loop control simulations and open-loop simulations that used primate neural data from robot-assisted reaching tasks. Main results. The HRL controller was able to perform classification and regression tasks using its episodic and sequential learning modes, respectively. In our experiments, the HRL controller quickly achieved convergence to an effective control policy, followed by robust performance. The controller also automatically stopped adapting the parameters after converging to a satisfactory control policy. Additionally, when the input neural vector was reorganized, the controller resumed adaptation to maintain performance. Significance. By estimating an evaluative feedback directly from the user, the HRL control algorithm may provide an efficient method for autonomous adaptation of neuroprosthetic systems. This method may enable the user to teach the controller the desired behavior using only a simple feedback signal.

  16. Towards autonomous neuroprosthetic control using Hebbian reinforcement learning.

    PubMed

    Mahmoudi, Babak; Pohlmeyer, Eric A; Prins, Noeline W; Geng, Shijia; Sanchez, Justin C

    2013-12-01

    Our goal was to design an adaptive neuroprosthetic controller that could learn the mapping from neural states to prosthetic actions and automatically adjust adaptation using only a binary evaluative feedback as a measure of desirability/undesirability of performance. Hebbian reinforcement learning (HRL) in a connectionist network was used for the design of the adaptive controller. The method combines the efficiency of supervised learning with the generality of reinforcement learning. The convergence properties of this approach were studied using both closed-loop control simulations and open-loop simulations that used primate neural data from robot-assisted reaching tasks. The HRL controller was able to perform classification and regression tasks using its episodic and sequential learning modes, respectively. In our experiments, the HRL controller quickly achieved convergence to an effective control policy, followed by robust performance. The controller also automatically stopped adapting the parameters after converging to a satisfactory control policy. Additionally, when the input neural vector was reorganized, the controller resumed adaptation to maintain performance. By estimating an evaluative feedback directly from the user, the HRL control algorithm may provide an efficient method for autonomous adaptation of neuroprosthetic systems. This method may enable the user to teach the controller the desired behavior using only a simple feedback signal.

  17. Slim Chance: A Weight Control Program for the Learning Disabled.

    ERIC Educational Resources Information Center

    Rotatori, Anthony J.; And Others

    1982-01-01

    A school-based diet program for learning disabled children stresses modifying eating behavior by learning alternative ways of interacting with the environment. Techniques stress the importance of self regulation, strategies for self monitoring, the establishment of realistic weight goals, use of stimulus control procedures, increased physical…

  18. Short-Term Memory, Executive Control, and Children's Route Learning

    ERIC Educational Resources Information Center

    Purser, Harry R. M.; Farran, Emily K.; Courbois, Yannick; Lemahieu, Axelle; Mellier, Daniel; Sockeel, Pascal; Blades, Mark

    2012-01-01

    The aim of this study was to investigate route-learning ability in 67 children aged 5 to 11 years and to relate route-learning performance to the components of Baddeley's model of working memory. Children carried out tasks that included measures of verbal and visuospatial short-term memory and executive control and also measures of verbal and…

  19. Learned Helplessness: A Theory for the Age of Personal Control.

    ERIC Educational Resources Information Center

    Peterson, Christopher; And Others

    Experiences with uncontrollable events may lead to the expectation that future events will elude control, resulting in disruptions in motivation, emotion, and learning. This text explores this phenomenon, termed learned helplessness, tracking it from its discovery to its entrenchment in the psychological canon. The volume summarizes and integrates…

  20. Short-Term Memory, Executive Control, and Children's Route Learning

    ERIC Educational Resources Information Center

    Purser, Harry R. M.; Farran, Emily K.; Courbois, Yannick; Lemahieu, Axelle; Mellier, Daniel; Sockeel, Pascal; Blades, Mark

    2012-01-01

    The aim of this study was to investigate route-learning ability in 67 children aged 5 to 11 years and to relate route-learning performance to the components of Baddeley's model of working memory. Children carried out tasks that included measures of verbal and visuospatial short-term memory and executive control and also measures of verbal and…

  1. Reinforcement Learning for the Adaptive Control of Perception and Action

    DTIC Science & Technology

    1992-02-01

    This dissertation applies reinforcement learning to the adaptive control of active sensory-motor systems. Active sensory-motor systems, in addition...distinct states in the external world. This phenomenon, called perceptual aliasing, is shown to destabilize existing reinforcement learning algorithms

  2. Impact of Cooperative Learning on Naval Air Traffic Controller Training.

    ERIC Educational Resources Information Center

    Holubec, Edythe; And Others

    1993-01-01

    Reports on a study of the impact of cooperative learning techniques, compared with traditional Navy instructional methods, on Navy air traffic controller trainees. Finds that cooperative learning methods improved higher level reasoning skills and resulted in no failures among the trainees. (CFR)

  3. Fuzzy self-learning control for magnetic servo system

    NASA Technical Reports Server (NTRS)

    Tarn, J. H.; Kuo, L. T.; Juang, K. Y.; Lin, C. E.

    1994-01-01

    It is known that an effective control system is the key condition for successful implementation of high-performance magnetic servo systems. Major issues to design such control systems are nonlinearity; unmodeled dynamics, such as secondary effects for copper resistance, stray fields, and saturation; and that disturbance rejection for the load effect reacts directly on the servo system without transmission elements. One typical approach to design control systems under these conditions is a special type of nonlinear feedback called gain scheduling. It accommodates linear regulators whose parameters are changed as a function of operating conditions in a preprogrammed way. In this paper, an on-line learning fuzzy control strategy is proposed. To inherit the wealth of linear control design, the relations between linear feedback and fuzzy logic controllers have been established. The exercise of engineering axioms of linear control design is thus transformed into tuning of appropriate fuzzy parameters. Furthermore, fuzzy logic control brings the domain of candidate control laws from linear into nonlinear, and brings new prospects into design of the local controllers. On the other hand, a self-learning scheme is utilized to automatically tune the fuzzy rule base. It is based on network learning infrastructure; statistical approximation to assign credit; animal learning method to update the reinforcement map with a fast learning rate; and temporal difference predictive scheme to optimize the control laws. Different from supervised and statistical unsupervised learning schemes, the proposed method learns on-line from past experience and information from the process and forms a rule base of an FLC system from randomly assigned initial control rules.

  4. Joint Machine Learning and Game Theory for Rate Control in High Efficiency Video Coding.

    PubMed

    Gao, Wei; Kwong, Sam; Jia, Yuheng

    2017-08-25

    In this paper, a joint machine learning and game theory modeling (MLGT) framework is proposed for inter frame coding tree unit (CTU) level bit allocation and rate control (RC) optimization in High Efficiency Video Coding (HEVC). First, a support vector machine (SVM) based multi-classification scheme is proposed to improve the prediction accuracy of CTU-level Rate-Distortion (R-D) model. The legacy "chicken-and-egg" dilemma in video coding is proposed to be overcome by the learning-based R-D model. Second, a mixed R-D model based cooperative bargaining game theory is proposed for bit allocation optimization, where the convexity of the mixed R-D model based utility function is proved, and Nash bargaining solution (NBS) is achieved by the proposed iterative solution search method. The minimum utility is adjusted by the reference coding distortion and frame-level Quantization parameter (QP) change. Lastly, intra frame QP and inter frame adaptive bit ratios are adjusted to make inter frames have more bit resources to maintain smooth quality and bit consumption in the bargaining game optimization. Experimental results demonstrate that the proposed MLGT based RC method can achieve much better R-D performances, quality smoothness, bit rate accuracy, buffer control results and subjective visual quality than the other state-of-the-art one-pass RC methods, and the achieved R-D performances are very close to the performance limits from the FixedQP method.

  5. Robust iterative methods

    SciTech Connect

    Saadd, Y.

    1994-12-31

    In spite of the tremendous progress achieved in recent years in the general area of iterative solution techniques, there are still a few obstacles to the acceptance of iterative methods in a number of applications. These applications give rise to very indefinite or highly ill-conditioned non Hermitian matrices. Trying to solve these systems with the simple-minded standard preconditioned Krylov subspace methods can be a frustrating experience. With the mathematical and physical models becoming more sophisticated, the typical linear systems which we encounter today are far more difficult to solve than those of just a few years ago. This trend is likely to accentuate. This workshop will discuss (1) these applications and the types of problems that they give rise to; and (2) recent progress in solving these problems with iterative methods. The workshop will end with a hopefully stimulating panel discussion with the speakers.

  6. Learning, attentional control and action video games

    PubMed Central

    Green, C.S.; Bavelier, D.

    2012-01-01

    While humans have an incredible capacity to acquire new skills and alter their behavior as a result of experience, enhancements in performance are typically narrowly restricted to the parameters of the training environment, with little evidence of generalization to different, even seemingly highly related, tasks. Such specificity is a major obstacle for the development of many real-world training or rehabilitation paradigms, which necessarily seek to promote more general learning. In contrast to these typical findings, research over the past decade has shown that training on ‘action video games’ produces learning that transfers well beyond the training task. This has led to substantial interest among those interested in rehabilitation, for instance, after stroke or to treat amblyopia, or training for various precision-demanding jobs, for instance, endoscopic surgery or piloting unmanned aerial drones. Although the predominant focus of the field has been on outlining the breadth of possible action-game-related enhancements, recent work has concentrated on uncovering the mechanisms that underlie these changes, an important first step towards the goal of designing and using video games for more definite purposes. Game playing may not convey an immediate advantage on new tasks (increased performance from the very first trial), but rather the true effect of action video game playing may be to enhance the ability to learn new tasks. Such a mechanism may serve as a signature of training regimens that are likely to produce transfer of learning. PMID:22440805

  7. Learning, attentional control, and action video games.

    PubMed

    Green, C S; Bavelier, D

    2012-03-20

    While humans have an incredible capacity to acquire new skills and alter their behavior as a result of experience, enhancements in performance are typically narrowly restricted to the parameters of the training environment, with little evidence of generalization to different, even seemingly highly related, tasks. Such specificity is a major obstacle for the development of many real-world training or rehabilitation paradigms, which necessarily seek to promote more general learning. In contrast to these typical findings, research over the past decade has shown that training on 'action video games' produces learning that transfers well beyond the training task. This has led to substantial interest among those interested in rehabilitation, for instance, after stroke or to treat amblyopia, or training for various precision-demanding jobs, for instance, endoscopic surgery or piloting unmanned aerial drones. Although the predominant focus of the field has been on outlining the breadth of possible action-game-related enhancements, recent work has concentrated on uncovering the mechanisms that underlie these changes, an important first step towards the goal of designing and using video games for more definite purposes. Game playing may not convey an immediate advantage on new tasks (increased performance from the very first trial), but rather the true effect of action video game playing may be to enhance the ability to learn new tasks. Such a mechanism may serve as a signature of training regimens that are likely to produce transfer of learning.

  8. Learner Control in Computer Assisted Learning.

    ERIC Educational Resources Information Center

    Holmes, N.; And Others

    1985-01-01

    An investigation of how secondary students coped when taught binary arithmetic through a computer assisted instruction program used four treatment groups: learner control, learner control with advice; random program control, and adaptive program control. The random group performed less well, but no differences were found between learner and…

  9. Iterated multidimensional wave conversion

    SciTech Connect

    Brizard, A. J.; Tracy, E. R.; Johnston, D.; Kaufman, A. N.; Richardson, A. S.; Zobin, N.

    2011-12-23

    Mode conversion can occur repeatedly in a two-dimensional cavity (e.g., the poloidal cross section of an axisymmetric tokamak). We report on two novel concepts that allow for a complete and global visualization of the ray evolution under iterated conversions. First, iterated conversion is discussed in terms of ray-induced maps from the two-dimensional conversion surface to itself (which can be visualized in terms of three-dimensional rooms). Second, the two-dimensional conversion surface is shown to possess a symplectic structure derived from Dirac constraints associated with the two dispersion surfaces of the interacting waves.

  10. Rescheduling with iterative repair

    NASA Technical Reports Server (NTRS)

    Zweben, Monte; Davis, Eugene; Daun, Brian; Deale, Michael

    1992-01-01

    This paper presents a new approach to rescheduling called constraint-based iterative repair. This approach gives our system the ability to satisfy domain constraints, address optimization concerns, minimize perturbation to the original schedule, produce modified schedules, quickly, and exhibits 'anytime' behavior. The system begins with an initial, flawed schedule and then iteratively repairs constraint violations until a conflict-free schedule is produced. In an empirical demonstration, we vary the importance of minimizing perturbation and report how fast the system is able to resolve conflicts in a given time bound. We also show the anytime characteristics of the system. These experiments were performed within the domain of Space Shuttle ground processing.

  11. Rescheduling with iterative repair

    NASA Technical Reports Server (NTRS)

    Zweben, Monte; Davis, Eugene; Daun, Brian; Deale, Michael

    1992-01-01

    This paper presents a new approach to rescheduling called constraint-based iterative repair. This approach gives our system the ability to satisfy domain constraints, address optimization concerns, minimize perturbation to the original schedule, and produce modified schedules quickly. The system begins with an initial, flawed schedule and then iteratively repairs constraint violations until a conflict-free schedule is produced. In an empirical demonstration, we vary the importance of minimizing perturbation and report how fast the system is able to resolve conflicts in a given time bound. These experiments were performed within the domain of Space Shuttle ground processing.

  12. Learning control for robotic manipulators with sparse data

    NASA Technical Reports Server (NTRS)

    Morita, Atsushi; Dubowsky, Steven; Hootsmans, Norbert A. M.

    1987-01-01

    Learning control algorithms have been proposed for error compensation in repetitive robotic manipulator tasks. It is shown that the performance of such control algorithms can be seriously degraded when the feedback data they use is relatively sparse in time, such as might be provided by vision systems. It is also shown that learning control algorithms can be modified to compensate for the effects of sparse data and thereby yield performance which approaches that of systems without limitations on the sensory information available for control.

  13. A Hierarchical Learning Control Framework for an Aerial Manipulation System

    NASA Astrophysics Data System (ADS)

    Ma, Le; Chi, yanxun; Li, Jiapeng; Li, Zhongsheng; Ding, Yalei; Liu, Lixing

    2017-07-01

    A hierarchical learning control framework for an aerial manipulation system is proposed. Firstly, the mechanical design of aerial manipulation system is introduced and analyzed, and the kinematics and the dynamics based on Newton-Euler equation are modeled. Secondly, the framework of hierarchical learning for this system is presented, in which flight platform and manipulator are controlled by different controller respectively. The RBF (Radial Basis Function) neural networks are employed to estimate parameters and control. The Simulation and experiment demonstrate that the methods proposed effective and advanced.

  14. Motor Skill Learning, Retention, and Control Deficits in Parkinson's Disease

    PubMed Central

    Pendt, Lisa Katharina; Reuter, Iris; Müller, Hermann

    2011-01-01

    Parkinson's disease, which affects the basal ganglia, is known to lead to various impairments of motor control. Since the basal ganglia have also been shown to be involved in learning processes, motor learning has frequently been investigated in this group of patients. However, results are still inconsistent, mainly due to skill levels and time scales of testing. To bridge across the time scale problem, the present study examined de novo skill learning over a long series of practice sessions that comprised early and late learning stages as well as retention. 19 non-demented, medicated, mild to moderate patients with Parkinson's disease and 19 healthy age and gender matched participants practiced a novel throwing task over five days in a virtual environment where timing of release was a critical element. Six patients and seven control participants came to an additional long-term retention testing after seven to nine months. Changes in task performance were analyzed by a method that differentiates between three components of motor learning prominent in different stages of learning: Tolerance, Noise and Covariation. In addition, kinematic analysis related the influence of skill levels as affected by the specific motor control deficits in Parkinson patients to the process of learning. As a result, patients showed similar learning in early and late stages compared to the control subjects. Differences occurred in short-term retention tests; patients' performance constantly decreased after breaks arising from poorer release timing. However, patients were able to overcome the initial timing problems within the course of each practice session and could further improve their throwing performance. Thus, results demonstrate the intact ability to learn a novel motor skill in non-demented, medicated patients with Parkinson's disease and indicate confounding effects of motor control deficits on retention performance. PMID:21760898

  15. Motor skill learning, retention, and control deficits in Parkinson's disease.

    PubMed

    Pendt, Lisa Katharina; Reuter, Iris; Müller, Hermann

    2011-01-01

    Parkinson's disease, which affects the basal ganglia, is known to lead to various impairments of motor control. Since the basal ganglia have also been shown to be involved in learning processes, motor learning has frequently been investigated in this group of patients. However, results are still inconsistent, mainly due to skill levels and time scales of testing. To bridge across the time scale problem, the present study examined de novo skill learning over a long series of practice sessions that comprised early and late learning stages as well as retention. 19 non-demented, medicated, mild to moderate patients with Parkinson's disease and 19 healthy age and gender matched participants practiced a novel throwing task over five days in a virtual environment where timing of release was a critical element. Six patients and seven control participants came to an additional long-term retention testing after seven to nine months. Changes in task performance were analyzed by a method that differentiates between three components of motor learning prominent in different stages of learning: Tolerance, Noise and Covariation. In addition, kinematic analysis related the influence of skill levels as affected by the specific motor control deficits in Parkinson patients to the process of learning. As a result, patients showed similar learning in early and late stages compared to the control subjects. Differences occurred in short-term retention tests; patients' performance constantly decreased after breaks arising from poorer release timing. However, patients were able to overcome the initial timing problems within the course of each practice session and could further improve their throwing performance. Thus, results demonstrate the intact ability to learn a novel motor skill in non-demented, medicated patients with Parkinson's disease and indicate confounding effects of motor control deficits on retention performance.

  16. E-learning: controlling costs and increasing value.

    PubMed

    Walsh, Kieran

    2015-04-01

    E-learning now accounts for a substantial proportion of medical education provision. This progress has required significant investment and this investment has in turn come under increasing scrutiny so that the costs of e-learning may be controlled and its returns maximised. There are multiple methods by which the costs of e-learning can be controlled and its returns maximised. This short paper reviews some of those methods that are likely to be most effective and that are likely to save costs without compromising quality. Methods might include accessing free or low-cost resources from elsewhere; create short learning resources that will work on multiple devices; using open source platforms to host content; using in-house faculty to create content; sharing resources between institutions; and promoting resources to ensure high usage. Whatever methods are used to control costs or increase value, it is most important to evaluate the impact of these methods.

  17. Learning and tuning fuzzy logic controllers through reinforcements

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.; Khedkar, Pratap

    1992-01-01

    This paper presents a new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system. In particular, our generalized approximate reasoning-based intelligent control (GARIC) architecture (1) learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward neural network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto et al. (1983) to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

  18. Learning and tuning fuzzy logic controllers through reinforcements

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.; Khedkar, Pratap

    1992-01-01

    This paper presents a new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system. In particular, our generalized approximate reasoning-based intelligent control (GARIC) architecture (1) learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward neural network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto et al. (1983) to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

  19. Learning and tuning fuzzy logic controllers through reinforcements

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.; Khedkar, Pratap

    1992-01-01

    A new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. In particular, our Generalized Approximate Reasoning-based Intelligent Control (GARIC) architecture: (1) learns and tunes a fuzzy logic controller even when only weak reinforcements, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto, Sutton, and Anderson to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and has demonstrated significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

  20. Plasma vertical stabilisation in ITER

    NASA Astrophysics Data System (ADS)

    Gribov, Y.; Kavin, A.; Lukash, V.; Khayrutdinov, R.; Huijsmans, G. T. A.; Loarte, A.; Snipes, J. A.; Zabeo, L.

    2015-07-01

    This paper describes the progress in analysis of the ITER plasma vertical stabilisation (VS) system since its design review in 2007-2008. Two indices characterising plasma VS were studied. These are (1) the maximum value of plasma vertical displacement due to free drift that can be stopped by the VS system and (2) the maximum root mean square value of low frequency noise in the dZ/dt measurement signal used in the VS feedback loop. The first VS index was calculated using the PET code for 15 MA plasmas with the nominal position and shape. The second VS index was studied with the DINA code in the most demanding simulations for plasma magnetic control of 15 MA scenarios with the fastest plasma current ramp-up and early X-point formation, the fastest plasma current ramp-down in a divertor configuration, and an H to L mode transition at the current flattop. The studies performed demonstrate that the VS in-vessel coils, adopted recently in the baseline design, significantly increase the range of plasma controllability in comparison with the stabilising systems VS1 and VS2, providing operating margins sufficient to achieve ITER's goals specified in the project requirements. Additionally two sets of the DINA code simulations were performed with the goal of assessment of the capability of the PF system with the VS in-vessel coils: (i) to control the position of runaway electrons generated during disruptions in 15 MA scenarios and (ii) to trigger ELMs in H-mode plasmas of 7.5 MA/2.65 T scenarios planned for the early phase of ITER operation. It was also shown that ferromagnetic structures of the vacuum vessel (ferromagnetic inserts) and test blanket modules insignificantly affect the plasma VS.

  1. Infection control in learning disability services.

    PubMed

    Huyton, Rita

    Services for people with a learning disability are provided by many sectors of the health and social care economy. These include: social services, health services, voluntary organisations, charities, private care agencies and family carers. Care interventions can take place in a variety of settings, from the client's own home to day care, respite care, educational establishments, workshops, social clubs, luncheon clubs, shared housing and the acute services.

  2. ITER Fusion Energy

    ScienceCinema

    Dr. Norbert Holtkamp

    2016-07-12

    ITER (in Latin “the way”) is designed to demonstrate the scientific and technological feasibility of fusion energy. Fusion is the process by which two light atomic nuclei combine to form a heavier over one and thus release energy. In the fusion process two isotopes of hydrogen – deuterium and tritium – fuse together to form a helium atom and a neutron. Thus fusion could provide large scale energy production without greenhouse effects; essentially limitless fuel would be available all over the world. The principal goals of ITER are to generate 500 megawatts of fusion power for periods of 300 to 500 seconds with a fusion power multiplication factor, Q, of at least 10. Q ? 10 (input power 50 MW / output power 500 MW). The ITER Organization was officially established in Cadarache, France, on 24 October 2007. The seven members engaged in the project – China, the European Union, India, Japan, Korea, Russia and the United States – represent more than half the world’s population. The costs for ITER are shared by the seven members. The cost for the construction will be approximately 5.5 billion Euros, a similar amount is foreseen for the twenty-year phase of operation and the subsequent decommissioning.

  3. An Iterative Angle Trisection

    ERIC Educational Resources Information Center

    Muench, Donald L.

    2007-01-01

    The problem of angle trisection continues to fascinate people even though it has long been known that it can't be done with straightedge and compass alone. However, for practical purposes, a good iterative procedure can get you as close as you want. In this note, we present such a procedure. Using only straightedge and compass, our procedure…

  4. Iterative software kernels

    SciTech Connect

    Duff, I.

    1994-12-31

    This workshop focuses on kernels for iterative software packages. Specifically, the three speakers discuss various aspects of sparse BLAS kernels. Their topics are: `Current status of user lever sparse BLAS`; Current status of the sparse BLAS toolkit`; and `Adding matrix-matrix and matrix-matrix-matrix multiply to the sparse BLAS toolkit`.

  5. Facts and fiction of learning systems. [decision making intelligent control

    NASA Technical Reports Server (NTRS)

    Saridis, G. N.

    1975-01-01

    The methodology that will provide the updated precision for the hardware control and the advanced decision making and planning in the software control is called learning systems and intelligent control. It was developed theoretically as an alternative for the nonsystematic heuristic approaches of artificial intelligence experiments and the inflexible formulation of modern optimal control methods. Its basic concepts are discussed and some feasibility studies of some practical applications are presented.

  6. Novel reinforcement learning approach for difficult control problems

    NASA Astrophysics Data System (ADS)

    Becus, Georges A.; Thompson, Edward A.

    1997-09-01

    We review work conducted over the past several years and aimed at developing reinforcement learning architectures for solving difficult control problems and based on and inspired by associative control process (ACP) networks. We briefly review ACP networks able to reproduce many classical instrumental conditioning test results observed in animal research and to engage in real-time, closed-loop, goal-seeking interactions with their environment. Chronologically, our contributions include the ideally interfaced ACP network which is endowed with hierarchical, attention, and failure recognition interface mechanisms which greatly enhanced the capabilities of the original ACP network. When solving the cart-pole problem, it achieves 100 percent reliability and a reduction in training time similar to that of Baird and Klopf's modified ACP network and additionally an order of magnitude reduction in number of failures experienced for successful training. Next we introduced the command and control center/internal drive (Cid) architecture for artificial neural learning systems. It consists of a hierarchy of command and control centers governing motor selection networks. Internal drives, similar hunger, thirst, or reproduction in biological systems, are formed within the controller to facilitate learning. Efficiency, reliability, and adjustability of this architecture were demonstrated on the benchmark cart-pole control problem. A comparison with other artificial learning systems indicates that it learns over 100 times faster than Barto, et al's adaptive search element/adaptive critic element, experiencing less failures by more than an order of magnitude while capable of being fine-tuned by the user, on- line, for improved performance without additional training. Finally we present work in progress on a 'peaks and valleys' scheme which moves away from the one-dimensional learning mechanism currently found in Cid and shows promises in solving even more difficult learning control

  7. Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: a Bayesian simulation.

    PubMed

    Bauer, Robert; Gharabaghi, Alireza

    2015-01-01

    Restorative brain-computer interfaces (BCI) are increasingly used to provide feedback of neuronal states in a bid to normalize pathological brain activity and achieve behavioral gains. However, patients and healthy subjects alike often show a large variability, or even inability, of brain self-regulation for BCI control, known as BCI illiteracy. Although current co-adaptive algorithms are powerful for assistive BCIs, their inherent class switching clashes with the operant conditioning goal of restorative BCIs. Moreover, due to the treatment rationale, the classifier of restorative BCIs usually has a constrained feature space, thus limiting the possibility of classifier adaptation. In this context, we applied a Bayesian model of neurofeedback and reinforcement learning for different threshold selection strategies to study the impact of threshold adaptation of a linear classifier on optimizing restorative BCIs. For each feedback iteration, we first determined the thresholds that result in minimal action entropy and maximal instructional efficiency. We then used the resulting vector for the simulation of continuous threshold adaptation. We could thus show that threshold adaptation can improve reinforcement learning, particularly in cases of BCI illiteracy. Finally, on the basis of information-theory, we provided an explanation for the achieved benefits of adaptive threshold setting.

  8. Physiotherapists use a great variety of motor learning options in neurological rehabilitation, from which they choose through an iterative process: a retrospective think-aloud study.

    PubMed

    Kleynen, Melanie; Moser, Albine; Haarsma, Frederike A; Beurskens, Anna J; Braun, Susy M

    2017-08-01

    The goal of this study was to examine which motor learning options are applied by experienced physiotherapists in neurological rehabilitation, and how they choose between the different options. A descriptive qualitative approach was used. A purposive sample of five expert physiotherapists from the neurological ward of a rehabilitation center participated. Data were collected using nine videotaped therapy situations. During retrospective think-aloud interviews, the physiotherapists were instructed to constantly "think aloud" while they were watching their own videos. Five "operators" were identified: "act", "know", "observe", "assess" and "argue". The "act" operator consisted of 34 motor learning options, which were clustered into "instruction", "feedback" and "organization". The "know", "observe", "assess" and "argue" operators explained how therapists chose one of these options. The four operators seem to be interrelated and together lead to a decision to apply a particular motor learning option. Results show that the participating physiotherapists used a great variety of motor learning options in their treatment sessions. Further, the decision-making process with regard to these motor learning options was identified. Results may support future intervention studies that match the content and process of therapy in daily practice. The study should be repeated with other physiotherapists. Implications for Rehabilitation The study provided insight into the way experienced therapist handle the great variety of possible motor learning options, including concrete ideas on how to operationalize these options in specific situations. Despite differences in patients' abilities, it seems that therapists use the same underlying clinical reasoning process when choosing a particular motor learning option. Participating physiotherapists used more than the in guidelines suggested motor learning options and considered more than the suggested factors, hence adding practice based

  9. Amygdala interneuron subtypes control fear learning through disinhibition.

    PubMed

    Wolff, Steffen B E; Gründemann, Jan; Tovote, Philip; Krabbe, Sabine; Jacobson, Gilad A; Müller, Christian; Herry, Cyril; Ehrlich, Ingrid; Friedrich, Rainer W; Letzkus, Johannes J; Lüthi, Andreas

    2014-05-22

    Learning is mediated by experience-dependent plasticity in neuronal circuits. Activity in neuronal circuits is tightly regulated by different subtypes of inhibitory interneurons, yet their role in learning is poorly understood. Using a combination of in vivo single-unit recordings and optogenetic manipulations, we show that in the mouse basolateral amygdala, interneurons expressing parvalbumin (PV) and somatostatin (SOM) bidirectionally control the acquisition of fear conditioning--a simple form of associative learning--through two distinct disinhibitory mechanisms. During an auditory cue, PV(+) interneurons are excited and indirectly disinhibit the dendrites of basolateral amygdala principal neurons via SOM(+) interneurons, thereby enhancing auditory responses and promoting cue-shock associations. During an aversive footshock, however, both PV(+) and SOM(+) interneurons are inhibited, which boosts postsynaptic footshock responses and gates learning. These results demonstrate that associative learning is dynamically regulated by the stimulus-specific activation of distinct disinhibitory microcircuits through precise interactions between different subtypes of local interneurons.

  10. A model for sensorimotor control and learning

    NASA Technical Reports Server (NTRS)

    Raibert, M. H.

    1978-01-01

    A model for motor learning, generalization, and adaptation is presented. It is shown that the equations of motion of a limb can be expressed in a parametric form that facilitates transformation of desired trajectories into plans. These parametric equations are used in conjunction with a quantized multi-dimensional memory organized by state variables. The memory is supplied with data derived from the analysis of practice movements. A small computer and mechanical arm are used to implement the model and study its properties. Results verify the ability to acquire new movements, adapt to mechanical loads, and generalize between similar movements.

  11. Discrete-Time Local Value Iteration Adaptive Dynamic Programming: Admissibility and Termination Analysis.

    PubMed

    Wei, Qinglai; Liu, Derong; Lin, Qiao

    2016-08-03

    In this paper, a novel local value iteration adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. The focuses of this paper are to study admissibility properties and the termination criteria of discrete-time local value iteration ADP algorithms. In the discrete-time local value iteration ADP algorithm, the iterative value functions and the iterative control laws are both updated in a given subset of the state space in each iteration, instead of the whole state space. For the first time, admissibility properties of iterative control laws are analyzed for the local value iteration ADP algorithm. New termination criteria are established, which terminate the iterative local ADP algorithm with an admissible approximate optimal control law. Finally, simulation results are given to illustrate the performance of the developed algorithm.

  12. Neuromorphic learning of continuous-valued mappings from noise-corrupted data. Application to real-time adaptive control

    NASA Technical Reports Server (NTRS)

    Troudet, Terry; Merrill, Walter C.

    1990-01-01

    The ability of feed-forward neural network architectures to learn continuous valued mappings in the presence of noise was demonstrated in relation to parameter identification and real-time adaptive control applications. An error function was introduced to help optimize parameter values such as number of training iterations, observation time, sampling rate, and scaling of the control signal. The learning performance depended essentially on the degree of embodiment of the control law in the training data set and on the degree of uniformity of the probability distribution function of the data that are presented to the net during sequence. When a control law was corrupted by noise, the fluctuations of the training data biased the probability distribution function of the training data sequence. Only if the noise contamination is minimized and the degree of embodiment of the control law is maximized, can a neural net develop a good representation of the mapping and be used as a neurocontroller. A multilayer net was trained with back-error-propagation to control a cart-pole system for linear and nonlinear control laws in the presence of data processing noise and measurement noise. The neurocontroller exhibited noise-filtering properties and was found to operate more smoothly than the teacher in the presence of measurement noise.

  13. Discrete-Time Nonzero-Sum Games for Multiplayer Using Policy-Iteration-Based Adaptive Dynamic Programming Algorithms.

    PubMed

    Zhang, Huaguang; Jiang, He; Luo, Chaomin; Xiao, Geyang

    2016-10-03

    In this paper, we investigate the nonzero-sum games for a class of discrete-time (DT) nonlinear systems by using a novel policy iteration (PI) adaptive dynamic programming (ADP) method. The main idea of our proposed PI scheme is to utilize the iterative ADP algorithm to obtain the iterative control policies, which not only ensure the system to achieve stability but also minimize the performance index function for each player. This paper integrates game theory, optimal control theory, and reinforcement learning technique to formulate and handle the DT nonzero-sum games for multiplayer. First, we design three actor-critic algorithms, an offline one and two online ones, for the PI scheme. Subsequently, neural networks are employed to implement these algorithms and the corresponding stability analysis is also provided via the Lyapunov theory. Finally, a numerical simulation example is presented to demonstrate the effectiveness of our proposed approach.

  14. Causal Learning in Gambling Disorder: Beyond the Illusion of Control.

    PubMed

    Perales, José C; Navas, Juan F; Ruiz de Lara, Cristian M; Maldonado, Antonio; Catena, Andrés

    2017-06-01

    Causal learning is the ability to progressively incorporate raw information about dependencies between events, or between one's behavior and its outcomes, into beliefs of the causal structure of the world. In spite of the fact that some cognitive biases in gambling disorder can be described as alterations of causal learning involving gambling-relevant cues, behaviors, and outcomes, general causal learning mechanisms in gamblers have not been systematically investigated. In the present study, we compared gambling disorder patients against controls in an instrumental causal learning task. Evidence of illusion of control, namely, overestimation of the relationship between one's behavior and an uncorrelated outcome, showed up only in gamblers with strong current symptoms. Interestingly, this effect was part of a more complex pattern, in which gambling disorder patients manifested a poorer ability to discriminate between null and positive contingencies. Additionally, anomalies were related to gambling severity and current gambling disorder symptoms. Gambling-related biases, as measured by a standard psychometric tool, correlated with performance in the causal learning task, but not in the expected direction. Indeed, performance of gamblers with stronger biases tended to resemble the one of controls, which could imply that anomalies of causal learning processes play a role in gambling disorder, but do not seem to underlie gambling-specific biases, at least in a simple, direct way.

  15. CORSICA modelling of ITER hybrid operation scenarios

    NASA Astrophysics Data System (ADS)

    Kim, S. H.; Bulmer, R. H.; Campbell, D. J.; Casper, T. A.; LoDestro, L. L.; Meyer, W. H.; Pearlstein, L. D.; Snipes, J. A.

    2016-12-01

    The hybrid operating mode observed in several tokamaks is characterized by further enhancement over the high plasma confinement (H-mode) associated with reduced magneto-hydro-dynamic (MHD) instabilities linked to a stationary flat safety factor (q ) profile in the core region. The proposed ITER hybrid operation is currently aiming at operating for a long burn duration (>1000 s) with a moderate fusion power multiplication factor, Q , of at least 5. This paper presents candidate ITER hybrid operation scenarios developed using a free-boundary transport modelling code, CORSICA, taking all relevant physics and engineering constraints into account. The ITER hybrid operation scenarios have been developed by tailoring the 15 MA baseline ITER inductive H-mode scenario. Accessible operation conditions for ITER hybrid operation and achievable range of plasma parameters have been investigated considering uncertainties on the plasma confinement and transport. ITER operation capability for avoiding the poloidal field coil current, field and force limits has been examined by applying different current ramp rates, flat-top plasma currents and densities, and pre-magnetization of the poloidal field coils. Various combinations of heating and current drive (H&CD) schemes have been applied to study several physics issues, such as the plasma current density profile tailoring, enhancement of the plasma energy confinement and fusion power generation. A parameterized edge pedestal model based on EPED1 added to the CORSICA code has been applied to hybrid operation scenarios. Finally, fully self-consistent free-boundary transport simulations have been performed to provide information on the poloidal field coil voltage demands and to study the controllability with the ITER controllers. Extended from Proc. 24th Int. Conf. on Fusion Energy (San Diego, 2012) IT/P1-13.

  16. A neural fuzzy controller learning by fuzzy error propagation

    NASA Technical Reports Server (NTRS)

    Nauck, Detlef; Kruse, Rudolf

    1992-01-01

    In this paper, we describe a procedure to integrate techniques for the adaptation of membership functions in a linguistic variable based fuzzy control environment by using neural network learning principles. This is an extension to our work. We solve this problem by defining a fuzzy error that is propagated back through the architecture of our fuzzy controller. According to this fuzzy error and the strength of its antecedent each fuzzy rule determines its amount of error. Depending on the current state of the controlled system and the control action derived from the conclusion, each rule tunes the membership functions of its antecedent and its conclusion. By this we get an unsupervised learning technique that enables a fuzzy controller to adapt to a control task by knowing just about the global state and the fuzzy error.

  17. A neural fuzzy controller learning by fuzzy error propagation

    NASA Technical Reports Server (NTRS)

    Nauck, Detlef; Kruse, Rudolf

    1992-01-01

    In this paper, we describe a procedure to integrate techniques for the adaptation of membership functions in a linguistic variable based fuzzy control environment by using neural network learning principles. This is an extension to our work. We solve this problem by defining a fuzzy error that is propagated back through the architecture of our fuzzy controller. According to this fuzzy error and the strength of its antecedent each fuzzy rule determines its amount of error. Depending on the current state of the controlled system and the control action derived from the conclusion, each rule tunes the membership functions of its antecedent and its conclusion. By this we get an unsupervised learning technique that enables a fuzzy controller to adapt to a control task by knowing just about the global state and the fuzzy error.

  18. Recent developments in learning control and system identification for robots and structures

    NASA Technical Reports Server (NTRS)

    Phan, M.; Juang, J.-N.; Longman, R. W.

    1990-01-01

    This paper reviews recent results in learning control and learning system identification, with particular emphasis on discrete-time formulation, and their relation to adaptive theory. Related continuous-time results are also discussed. Among the topics presented are proportional, derivative, and integral learning controllers, time-domain formulation of discrete learning algorithms. Newly developed techniques are described including the concept of the repetition domain, and the repetition domain formulation of learning control by linear feedback, model reference learning control, indirect learning control with parameter estimation, as well as related basic concepts, recursive and non-recursive methods for learning identification.

  19. Advances in Neuroprosthetic Learning and Control

    PubMed Central

    Carmena, Jose M.

    2013-01-01

    Significant progress has occurred in the field of brain–machine interfaces (BMI) since the first demonstrations with rodents, monkeys, and humans controlling different prosthetic devices directly with neural activity. This technology holds great potential to aid large numbers of people with neurological disorders. However, despite this initial enthusiasm and the plethora of available robotic technologies, existing neural interfaces cannot as yet master the control of prosthetic, paralyzed, or otherwise disabled limbs. Here I briefly discuss recent advances from our laboratory into the neural basis of BMIs that should lead to better prosthetic control and clinically viable solutions, as well as new insights into the neurobiology of action. PMID:23700383

  20. Advances in neuroprosthetic learning and control.

    PubMed

    Carmena, Jose M

    2013-01-01

    Significant progress has occurred in the field of brain-machine interfaces (BMI) since the first demonstrations with rodents, monkeys, and humans controlling different prosthetic devices directly with neural activity. This technology holds great potential to aid large numbers of people with neurological disorders. However, despite this initial enthusiasm and the plethora of available robotic technologies, existing neural interfaces cannot as yet master the control of prosthetic, paralyzed, or otherwise disabled limbs. Here I briefly discuss recent advances from our laboratory into the neural basis of BMIs that should lead to better prosthetic control and clinically viable solutions, as well as new insights into the neurobiology of action.

  1. Optimal control of a coupled partial and ordinary differential equations system for the assimilation of polarimetry Stokes vector measurements in tokamak free-boundary equilibrium reconstruction with application to ITER

    NASA Astrophysics Data System (ADS)

    Faugeras, Blaise; Blum, Jacques; Heumann, Holger; Boulbe, Cédric

    2017-08-01

    The modelization of polarimetry Faraday rotation measurements commonly used in tokamak plasma equilibrium reconstruction codes is an approximation to the Stokes model. This approximation is not valid for the foreseen ITER scenarios where high current and electron density plasma regimes are expected. In this work a method enabling the consistent resolution of the inverse equilibrium reconstruction problem in the framework of non-linear free-boundary equilibrium coupled to the Stokes model equation for polarimetry is provided. Using optimal control theory we derive the optimality system for this inverse problem. A sequential quadratic programming (SQP) method is proposed for its numerical resolution. Numerical experiments with noisy synthetic measurements in the ITER tokamak configuration for two test cases, the second of which is an H-mode plasma, show that the method is efficient and that the accuracy of the identification of the unknown profile functions is improved compared to the use of classical Faraday measurements.

  2. An Iterative Learning Control Approach to Improving Fidelity in Internet-Distributed Hardware-in-the-Loop Simulation

    DTIC Science & Technology

    2012-06-15

    converter, transmission, and shift logic. The torque converter model is a static model that takes pump and turbine speeds as inputs and generates...pump and turbine torques according to the equations ( ) ( ) ( ) 2 1 pump pump r r turbine r pump sign ω τ ω κ ω τ α ω τ   = −     = (12...where /r turbine pumpω ω ω= is the speed ratio between turbine and pump speeds, ( )rκ ω is a piecewise function approximating a desired capacity

  3. CD process control through machine learning

    NASA Astrophysics Data System (ADS)

    Utzny, Clemens

    2016-10-01

    For the specific requirements of the 14nm and 20nm site applications a new CD map approach was developed at the AMTC. This approach relies on a well established machine learning technique called recursive partitioning. Recursive partitioning is a powerful technique which creates a decision tree by successively testing whether the quantity of interest can be explained by one of the supplied covariates. The test performed is generally a statistical test with a pre-supplied significance level. Once the test indicates significant association between the variable of interest and a covariate a split performed at a threshold value which minimizes the variation within the newly attained groups. This partitioning is recurred until either no significant association can be detected or the resulting sub group size falls below a pre-supplied level.

  4. The physics role of ITER

    SciTech Connect

    Rutherford, P.H.

    1997-04-01

    Experimental research on the International Thermonuclear Experimental Reactor (ITER) will go far beyond what is possible on present-day tokamaks to address new and challenging issues in the physics of reactor-like plasmas. First and foremost, experiments in ITER will explore the physics issues of burning plasmas--plasmas that are dominantly self-heated by alpha-particles created by the fusion reactions themselves. Such issues will include (i) new plasma-physical effects introduced by the presence within the plasma of an intense population of energetic alpha particles; (ii) the physics of magnetic confinement for a burning plasma, which will involve a complex interplay of transport, stability and an internal self-generated heat source; and (iii) the physics of very-long-pulse/steady-state burning plasmas, in which much of the plasma current is also self-generated and which will require effective control of plasma purity and plasma-wall interactions. Achieving and sustaining burning plasma regimes in a tokamak necessarily requires plasmas that are larger than those in present experiments and have higher energy content and power flow, as well as much longer pulse length. Accordingly, the experimental program on ITER will embrace the study of issues of plasma physics and plasma-materials interactions that are specific to a reactor-scale fusion experiment. Such issues will include (i) confinement physics for a tokamak in which, for the first time, the core-plasma and the edge-plasma are simultaneously in a reactor-like regime; (ii) phenomena arising during plasma transients, including so-called disruptions, in regimes of high plasma current and thermal energy; and (iii) physics of a radiative divertor designed for handling high power flow for long pulses, including novel plasma and atomic-physics effects as well as materials science of surfaces subject to intense plasma interaction. Experiments on ITER will be conducted by researchers in control rooms situated at major

  5. A fresh look at electron cyclotron current drive power requirements for stabilization of tearing modes in ITER

    NASA Astrophysics Data System (ADS)

    La Haye, R. J.

    2015-12-01

    ITER is an international project to design and build an experimental fusion reactor based on the "tokamak" concept. ITER relies upon localized electron cyclotron current drive (ECCD) at the rational safety factor q=2 to suppress or stabilize the expected poloidal mode m=2, toroidal mode n=1 neoclassical tearing mode (NTM) islands. Such islands if unmitigated degrade energy confinement, lock to the resistive wall (stop rotating), cause loss of "H-mode" and induce disruption. The International Tokamak Physics Activity (ITPA) on MHD, Disruptions and Magnetic Control joint experiment group MDC-8 on Current Drive Prevention/Stabilization of Neoclassical Tearing Modes started in 2005, after which assessments were made for the requirements for ECCD needed in ITER, particularly that of rf power and alignment on q=2 [1]. Narrow well-aligned rf current parallel to and of order of one percent of the total plasma current is needed to replace the "missing" current in the island O-points and heal or preempt (avoid destabilization by applying ECCD on q=2 in absence of the mode) the island [2-4]. This paper updates the advances in ECCD stabilization on NTMs learned in DIII-D experiments and modeling during the last 5 to 10 years as applies to stabilization by localized ECCD of tearing modes in ITER. This includes the ECCD (inside the q=1 radius) stabilization of the NTM "seeding" instability known as sawteeth (m/n=1/1) [5]. Recent measurements in DIII-D show that the ITER-similar current profile is classically unstable, curvature stabilization must not be neglected, and the small island width stabilization effect from helical ion polarization currents is stronger than was previously thought [6]. The consequences of updated assumptions in ITER modeling of the minimum well-aligned ECCD power needed are all-in-all favorable (and well-within the ITER 24 gyrotron capability) when all effects are included. However, a "wild card" may be broadening of the localized ECCD by the presence of

  6. A fresh look at electron cyclotron current drive power requirements for stabilization of tearing modes in ITER

    SciTech Connect

    La Haye, R. J.

    2015-12-10

    ITER is an international project to design and build an experimental fusion reactor based on the “tokamak” concept. ITER relies upon localized electron cyclotron current drive (ECCD) at the rational safety factor q=2 to suppress or stabilize the expected poloidal mode m=2, toroidal mode n=1 neoclassical tearing mode (NTM) islands. Such islands if unmitigated degrade energy confinement, lock to the resistive wall (stop rotating), cause loss of “H-mode” and induce disruption. The International Tokamak Physics Activity (ITPA) on MHD, Disruptions and Magnetic Control joint experiment group MDC-8 on Current Drive Prevention/Stabilization of Neoclassical Tearing Modes started in 2005, after which assessments were made for the requirements for ECCD needed in ITER, particularly that of rf power and alignment on q=2 [1]. Narrow well-aligned rf current parallel to and of order of one percent of the total plasma current is needed to replace the “missing” current in the island O-points and heal or preempt (avoid destabilization by applying ECCD on q=2 in absence of the mode) the island [2-4]. This paper updates the advances in ECCD stabilization on NTMs learned in DIII-D experiments and modeling during the last 5 to 10 years as applies to stabilization by localized ECCD of tearing modes in ITER. This includes the ECCD (inside the q=1 radius) stabilization of the NTM “seeding” instability known as sawteeth (m/n=1/1) [5]. Recent measurements in DIII-D show that the ITER-similar current profile is classically unstable, curvature stabilization must not be neglected, and the small island width stabilization effect from helical ion polarization currents is stronger than was previously thought [6]. The consequences of updated assumptions in ITER modeling of the minimum well-aligned ECCD power needed are all-in-all favorable (and well-within the ITER 24 gyrotron capability) when all effects are included. However, a “wild card” may be broadening of the localized

  7. Feasibility of low-concentration iodinated contrast medium with lower-tube-voltage dual-source CT aortography using iterative reconstruction: comparison with automatic exposure control CT aortography.

    PubMed

    Shin, Hee Jeong; Kim, Song Soo; Lee, Jae-Hwan; Park, Jae-Hyeong; Jeong, Jin-Ok; Jin, Seon Ah; Shin, Byung Seok; Shin, Kyung-Sook; Ahn, Moonsang

    2016-06-01

    To evaluate the feasibility of low-concentration contrast medium (CM) for vascular enhancement, image quality, and radiation dose on computed tomography aortography (CTA) using a combined low-tube-voltage and iterative reconstruction (IR) technique. Ninety subjects underwent dual-source CT (DSCT) operating in dual-source, high-pitch mode. DSCT scans were performed using both high-concentration CM (Group A, n = 50; Iomeprol 400) and low-concentration CM (Group B, n = 40; Iodixanol 270). Group A was scanned using a reference tube potential of 120 kVp and 120 reference mAs under automatic exposure control with IR. Group B was scanned using low-tube-voltage (80 or 100 kVp if body mass index ≥25 kg/m(2)) at a fixed current of 150 mAs, along with IR. Images of the two groups were compared regarding attenuation, image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), iodine load, and radiation dose in various locations of the CTA. In comparison between Group A and Group B, the average mean attenuation (454.73 ± 86.66 vs. 515.96 ± 101.55 HU), SNR (25.28 ± 4.34 vs. 31.29 ± 4.58), and CNR (21.83 ± 4.20 vs. 27.55 ± 4.81) on CTA in Group B showed significantly greater values and significantly lower image noise values (18.76 ± 2.19 vs. 17.48 ± 3.34) than those in Group A (all Ps < 0.05). Homogeneous contrast enhancement from the ascending thoracic aorta to the infrarenal abdominal aorta was significantly superior in Group B (P < 0.05). Low-concentration CM and a low-tube-voltage combination technique using IR is a feasible method, showing sufficient contrast enhancement and image quality.

  8. Can unconscious knowledge allow control in sequence learning?

    PubMed

    Fu, Qiufang; Dienes, Zoltán; Fu, Xiaolan

    2010-03-01

    This paper investigates the conscious status of both the knowledge that an item is legal (judgment knowledge) and the knowledge of why it is legal (structural knowledge) in sequence learning. We compared ability to control use of knowledge (Process Dissociation Procedure) with stated awareness of the knowledge (subjective measures) as measures of the conscious status of knowledge. Experiment 1 showed that when people could control use of judgment knowledge they were indeed conscious of having that knowledge according to their own statements. Yet Experiment 2 showed that people could exert such control over the use of judgment knowledge when claiming they had no structural knowledge: i.e. conscious judgment knowledge could be based on unconscious structural knowledge. Further implicit learning research should be clear over whether judgment or structural knowledge is claimed to be unconscious as the two dissociate in sequence learning.

  9. Can we (control) Engineer the degree learning process?

    NASA Astrophysics Data System (ADS)

    White, A. S.; Censlive, M.; Neilsen, D.

    2014-07-01

    This paper investigates how control theory could be applied to learning processes in engineering education. The initial point for the analysis is White's Double Loop learning model of human automation control modified for the education process where a set of governing principals is chosen, probably by the course designer. After initial training the student decides unknowingly on a mental map or model. After observing how the real world is behaving, a strategy to achieve the governing variables is chosen and a set of actions chosen. This may not be a conscious operation, it maybe completely instinctive. These actions will cause some consequences but not until a certain time delay. The current model is compared with the work of Hollenbeck on goal setting, Nelson's model of self-regulation and that of Abdulwahed, Nagy and Blanchard at Loughborough who investigated control methods applied to the learning process.

  10. ITER breeding blanket design

    SciTech Connect

    Gohar, Y.; Cardella, A.; Ioki, K.; Lousteau, D.; Mohri, K.; Raffray, R.; Zolti, E.

    1995-12-31

    A breeding blanket design has been developed for ITER to provide the necessary tritium fuel to achieve the technical objectives of the Enhanced Performance Phase. It uses a ceramic breeder and water coolant for compatibility with the ITER machine design of the Basic Performance Phase. Lithium zirconate and lithium oxide am the selected ceramic breeders based on the current data base. Enriched lithium and beryllium neutron multiplier are used for both breeders. Both forms of beryllium material, blocks and pebbles are used at different blanket locations based on thermo-mechanical considerations and beryllium thickness requirements. Type 316LN austenitic steel is used as structural material similar to the shielding blanket. Design issues and required R&D data are identified during the development of the design.

  11. The Iterate Manual

    DTIC Science & Technology

    1990-10-01

    is probably a bad idea. A better versica would use a temporary: (defmacro sum-of-squares (expr) (let ((temp ( gensym ))) ’(lot (,temp ,expr)) (sum...val ( gensym )) (tempi ( gensym )) (temp2 ( gensym )) (winner (or var iterate::*result-var*))) ’(progn (with ,max-val - nil) (with ,winner = nil) (cond ((null...the elements of a vector (disregards fill-pointer)" (let ((vect ( gensym )) (end ( gensym )) (index ( gensym ))) ’(progn (with ,vect - v) (with ,end = (array

  12. Iterative initial condition reconstruction

    NASA Astrophysics Data System (ADS)

    Schmittfull, Marcel; Baldauf, Tobias; Zaldarriaga, Matias

    2017-07-01

    Motivated by recent developments in perturbative calculations of the nonlinear evolution of large-scale structure, we present an iterative algorithm to reconstruct the initial conditions in a given volume starting from the dark matter distribution in real space. In our algorithm, objects are first moved back iteratively along estimated potential gradients, with a progressively reduced smoothing scale, until a nearly uniform catalog is obtained. The linear initial density is then estimated as the divergence of the cumulative displacement, with an optional second-order correction. This algorithm should undo nonlinear effects up to one-loop order, including the higher-order infrared resummation piece. We test the method using dark matter simulations in real space. At redshift z =0 , we find that after eight iterations the reconstructed density is more than 95% correlated with the initial density at k ≤0.35 h Mpc-1 . The reconstruction also reduces the power in the difference between reconstructed and initial fields by more than 2 orders of magnitude at k ≤0.2 h Mpc-1 , and it extends the range of scales where the full broadband shape of the power spectrum matches linear theory by a factor of 2-3. As a specific application, we consider measurements of the baryonic acoustic oscillation (BAO) scale that can be improved by reducing the degradation effects of large-scale flows. In our idealized dark matter simulations, the method improves the BAO signal-to-noise ratio by a factor of 2.7 at z =0 and by a factor of 2.5 at z =0.6 , improving standard BAO reconstruction by 70% at z =0 and 30% at z =0.6 , and matching the optimal BAO signal and signal-to-noise ratio of the linear density in the same volume. For BAO, the iterative nature of the reconstruction is the most important aspect.

  13. Enhancing Hebbian Learning to Control Brain Oscillatory Activity.

    PubMed

    Soekadar, Surjo R; Witkowski, Matthias; Birbaumer, Niels; Cohen, Leonardo G

    2015-09-01

    Sensorimotor rhythms (SMR, 8-15 Hz) are brain oscillations associated with successful motor performance, imagery, and imitation. Voluntary modulation of SMR can be used to control brain-machine interfaces (BMI) in the absence of any physical movements. The mechanisms underlying acquisition of such skill are unknown. Here, we provide evidence for a causal link between function of the primary motor cortex (M1), active during motor skill learning and retention, and successful acquisition of abstract skills such as control over SMR. Thirty healthy participants were trained on 5 consecutive days to control SMR oscillations. Each participant was randomly assigned to one of 3 groups that received either 20 min of anodal, cathodal, or sham transcranial direct current stimulation (tDCS) over M1. Learning SMR control across training days was superior in the anodal tDCS group relative to the other 2. Cathodal tDCS blocked the beneficial effects of training, as evidenced with sham tDCS. One month later, the newly acquired skill remained superior in the anodal tDCS group. Thus, application of weak electric currents of opposite polarities over M1 differentially modulates learning SMR control, pointing to this primary cortical region as a common substrate for acquisition of physical motor skills and learning to control brain oscillatory activity. Published by Oxford University Press 2014. This work is written by (a) US Government employee(s) and is in the public domain in the US.

  14. Cryogenic instrumentation for ITER magnets

    NASA Astrophysics Data System (ADS)

    Poncet, J.-M.; Manzagol, J.; Attard, A.; André, J.; Bizel-Bizellot, L.; Bonnay, P.; Ercolani, E.; Luchier, N.; Girard, A.; Clayton, N.; Devred, A.; Huygen, S.; Journeaux, J.-Y.

    2017-02-01

    Accurate measurements of the helium flowrate and of the temperature of the ITER magnets is of fundamental importance to make sure that the magnets operate under well controlled and reliable conditions, and to allow suitable helium flow distribution in the magnets through the helium piping. Therefore, the temperature and flow rate measurements shall be reliable and accurate. In this paper, we present the thermometric chains as well as the venturi flow meters installed in the ITER magnets and their helium piping. The presented thermometric block design is based on the design developed by CERN for the LHC, which has been further optimized via thermal simulations carried out by CEA. The electronic part of the thermometric chain was entirely developed by the CEA and will be presented in detail: it is based on a lock-in measurement and small signal amplification, and also provides a web interface and software to an industrial PLC. This measuring device provides a reliable, accurate, electromagnetically immune, and fast (up to 100 Hz bandwidth) system for resistive temperature sensors between a few ohms to 100 kΩ. The flowmeters (venturi type) which make up part of the helium mass flow measurement chain have been completely designed, and manufacturing is on-going. The behaviour of the helium gas has been studied in detailed thanks to ANSYS CFX software in order to obtain the same differential pressure for all types of flowmeters. Measurement uncertainties have been estimated and the influence of input parameters has been studied. Mechanical calculations have been performed to guarantee the mechanical strength of the venturis required for pressure equipment operating in nuclear environment. In order to complete the helium mass flow measurement chain, different technologies of absolute and differential pressure sensors have been tested in an applied magnetic field to identify equipment compatible with the ITER environment.

  15. Neutron cameras for ITER

    SciTech Connect

    Johnson, L.C.; Barnes, C.W.; Batistoni, P.

    1998-12-31

    Neutron cameras with horizontal and vertical views have been designed for ITER, based on systems used on JET and TFTR. The cameras consist of fan-shaped arrays of collimated flight tubes, with suitably chosen detectors situated outside the biological shield. The sight lines view the ITER plasma through slots in the shield blanket and penetrate the vacuum vessel, cryostat, and biological shield through stainless steel windows. This paper analyzes the expected performance of several neutron camera arrangements for ITER. In addition to the reference designs, the authors examine proposed compact cameras, in which neutron fluxes are inferred from {sup 16}N decay gammas in dedicated flowing water loops, and conventional cameras with fewer sight lines and more limited fields of view than in the reference designs. It is shown that the spatial sampling provided by the reference designs is sufficient to satisfy target measurement requirements and that some reduction in field of view may be permissible. The accuracy of measurements with {sup 16}N-based compact cameras is not yet established, and they fail to satisfy requirements for parameter range and time resolution by large margins.

  16. Neutron activation for ITER

    SciTech Connect

    Barnes, C.W.; Loughlin, M.J.; Nishitani, Takeo

    1996-04-29

    There are three primary goals for the Neutron Activation system for ITER: maintain a robust relative measure of fusion power with stability and high dynamic range (7 orders of magnitude); allow an absolute calibration of fusion power (energy); and provide a flexible and reliable system for materials testing. The nature of the activation technique is such that stability and high dynamic range can be intrinsic properties of the system. It has also been the technique that demonstrated (on JET and TFTR) the highest accuracy neutron measurements in DT operation. Since the gamma-ray detectors are not located on the tokamak and are therefore amenable to accurate characterization, and if material foils are placed very close to the ITER plasma with minimum scattering or attenuation, high overall accuracy in the fusion energy production (7--10%) should be achievable on ITER. In the paper, a conceptual design is presented. A system is shown to be capable of meeting these three goals, also detailed design issues remain to be solved.

  17. Controls Advanced Research Turbine: Lessons Learned during Advanced Controls Testing

    SciTech Connect

    Johnson, K.; Fingersh, L. J.; Wright, A.

    2005-06-01

    This paper describes some of the problems encountered while conducting tests on the Controls Advanced Research Turbine installed at the National Wind Technology Center. It also discusses some of the peculiarities of the C-code used to control the CART and discusses the fault protection routine, Turbine safety, and some of its failures.

  18. Learning Switching Control: A Tank Level-Control Exercise

    ERIC Educational Resources Information Center

    Pasamontes, M.; Alvarez, J. D.; Guzman, J. L.; Berenguel, M.

    2012-01-01

    A key topic in multicontroller strategies is the mechanism for switching between controllers, depending on the current operating point. The objective of the switching mechanism is to keep the control action coherent. To help students understand the switching strategy involved in multicontroller schema and the relationship between the system…

  19. Learning Switching Control: A Tank Level-Control Exercise

    ERIC Educational Resources Information Center

    Pasamontes, M.; Alvarez, J. D.; Guzman, J. L.; Berenguel, M.

    2012-01-01

    A key topic in multicontroller strategies is the mechanism for switching between controllers, depending on the current operating point. The objective of the switching mechanism is to keep the control action coherent. To help students understand the switching strategy involved in multicontroller schema and the relationship between the system…

  20. Online Learning ARMA Controllers With Guaranteed Closed-Loop Stability.

    PubMed

    Sahin, Savas; Guzelis, Cuneyt

    2016-11-01

    This paper presents a novel online block adaptive learning algorithm for autoregressive moving average (ARMA) controller design based on the real data measured from the plant. The method employs ARMA input-output models both for the plant and the resulting closed-loop system. In a sliding window, the plant model parameters are identified first offline using a supervised learning algorithm minimizing an ε -insensitive and regularized identification error, which is the window average of the distances between the measured plant output and the model output for the input provided by the controller. The optimal controller parameters are then determined again offline for another sliding window as the solution to a constrained optimization problem, where the cost is the ε -insensitive and regularized output tracking error and the constraints that are linear inequalities of the controller parameters are imposed for ensuring the closed-loop system to be Schur stable. Not only the identification phase but also the controller design phase uses the input-output samples measured from the plant during online learning. In the developed online controller design method, the controller parameters can always be kept in a parameter region providing Schur stability for the closed-loop system. The ε -insensitiveness provides robustness against disturbances, so does the regularization better generalization performance in the identification and the control. The method is tested on benchmark plants, including the inverted pendulum and dc motor models. The method is also tested on an emulated and also a real dc motor by online block adaptive learning ARMA controllers, in particular, Proportional-Integral-Derivative controllers.

  1. Energetic particle physics issues for ITER

    SciTech Connect

    Cheng, C.Z.; Budny, R.; Fu, G.Y.

    1996-12-31

    This paper summarizes our present understanding of the following energetic/alpha particle physics issues for the 21 MA, 20 TF coil ITER Interim Design configuration and operational scenarios: (a) toroidal field ripple effects on alpha particle confinement, (b) energetic particle interaction with low frequency MHD modes, (c) energetic particle excitation of toroidal Alfven eigenmodes, and (d) energetic particle transport due to MHD modes. TF ripple effects on alpha loss in ITER under a number of different operating conditions are found to be small with a maximum loss of 1%. With careful plasma control in ITER reversed-shear operation, TF ripple induced alpha loss can be reduced to below the nominal ITER design limit of 5%. Fishbone modes are expected to be unstable for {beta}{sub {alpha}} > 1%, and sawtooth stabilization is lost if the ideal kink growth rate exceeds 10% of the deeply trapped alpha precessional drift frequency evaluated at the q = 1 surface. However, it is expected that the fishbone modes will lead only to a local flattening of the alpha profile due to small banana size. MHD modes observed during slow decrease of stored energy after fast partial electron temperature collapse in JT-60U reversed-shear experiments may be resonant type instabilities; they may have implications on the energetic particle confinement in ITER reversed-shear operation. From the results of various TAE stability code calculations, ITER equilibria appear to lie close to TAE linear stability thresholds. However, the prognosis depends strongly on q profile and profiles of alpha and other high energy particles species. If TAE modes are unstable in ITER, the stochastic diffusion is the main loss mechanism, which scales with ({delta}B{sub r}/B){sup 2}, because of the relatively small alpha particle banana orbit size. For isolated TAE modes the particle loss is very small, and TAE modes saturate via the resonant wave-particle trapping process at very small amplitude.

  2. Instructional control of reinforcement learning: a behavioral and neurocomputational investigation.

    PubMed

    Doll, Bradley B; Jacobs, W Jake; Sanfey, Alan G; Frank, Michael J

    2009-11-24

    Humans learn how to behave directly through environmental experience and indirectly through rules and instructions. Behavior analytic research has shown that instructions can control behavior, even when such behavior leads to sub-optimal outcomes (Hayes, S. (Ed.). 1989. Rule-governed behavior: cognition, contingencies, and instructional control. Plenum Press.). Here we examine the control of behavior through instructions in a reinforcement learning task known to depend on striatal dopaminergic function. Participants selected between probabilistically reinforced stimuli, and were (incorrectly) told that a specific stimulus had the highest (or lowest) reinforcement probability. Despite experience to the contrary, instructions drove choice behavior. We present neural network simulations that capture the interactions between instruction-driven and reinforcement-driven behavior via two potential neural circuits: one in which the striatum is inaccurately trained by instruction representations coming from prefrontal cortex/hippocampus (PFC/HC), and another in which the striatum learns the environmentally based reinforcement contingencies, but is "overridden" at decision output. Both models capture the core behavioral phenomena but, because they differ fundamentally on what is learned, make distinct predictions for subsequent behavioral and neuroimaging experiments. Finally, we attempt to distinguish between the proposed computational mechanisms governing instructed behavior by fitting a series of abstract "Q-learning" and Bayesian models to subject data. The best-fitting model supports one of the neural models, suggesting the existence of a "confirmation bias" in which the PFC/HC system trains the reinforcement system by amplifying outcomes that are consistent with instructions while diminishing inconsistent outcomes.

  3. Active route learning in virtual environments: disentangling movement control from intention, instruction specificity, and navigation control.

    PubMed

    von Stülpnagel, Rul; Steffens, Melanie C

    2013-09-01

    Active navigation research examines how physiological and psychological involvement in navigation benefits spatial learning. However, existing conceptualizations of active navigation comprise separable, distinct factors. This research disentangles the contributions of movement control (i.e., self-contained vs. observed movement) as a central factor from learning intention (Experiment 1), instruction specificity and instruction control (Experiment 2), as well as navigation control (Experiment 3) to spatial learning in virtual environments. We tested the effects of these factors on landmark recognition (landmark knowledge), tour-integration and route navigation (route knowledge). Our findings suggest that movement control leads to robust advantages in landmark knowledge as compared to observed movement. Advantages in route knowledge do not depend on learning intention, but on the need to elaborate spatial information. Whenever the necessary level of elaboration is assured for observed movement, too, the development of route knowledge is not inferior to that for self-contained movement.

  4. On-line controlled documents: Lessons learned

    SciTech Connect

    Cochrell, R.C.; Steele, C.M.

    1995-06-01

    Placing Controlled Documents on-line on a computer network seems like the solution to many problems, one being distribution, with a path toward a paperless office. However, many problems presented themselves as we were designing the system and placing the documents on-line. Although we planned and established a Process Management Team to help work out the bugs, we still encountered many obstacles in the process. This presentation will cover the ``trials and tribulations`` of placing Controlled Documents on a computer network at three different sites. We will discuss the process we went through, the problems we encountered, the software we used, and how we got management to buy into the process.

  5. Iterative least squares functional networks classifier.

    PubMed

    El-Sebakhy, Emad A; Hadi, Ali S; Faisal, Kanaan A

    2007-05-01

    This paper proposes unconstrained functional networks as a new classifier to deal with the pattern recognition problems. Both methodology and learning algorithm for this kind of computational intelligence classifier using the iterative least squares optimization criterion are derived. The performance of this new intelligent systems scheme is demonstrated and examined using real-world applications. A comparative study with the most common classification algorithms in both machine learning and statistics communities is carried out. The study was achieved with only sets of second-order linearly independent polynomial functions to approximate the neuron functions. The results show that this new framework classifier is reliable, flexible, stable, and achieves a high-quality performance.

  6. ETR/ITER systems code

    SciTech Connect

    Barr, W.L.; Bathke, C.G.; Brooks, J.N.; Bulmer, R.H.; Busigin, A.; DuBois, P.F.; Fenstermacher, M.E.; Fink, J.; Finn, P.A.; Galambos, J.D.; Gohar, Y.; Gorker, G.E.; Haines, J.R.; Hassanein, A.M.; Hicks, D.R.; Ho, S.K.; Kalsi, S.S.; Kalyanam, K.M.; Kerns, J.A.; Lee, J.D.; Miller, J.R.; Miller, R.L.; Myall, J.O.; Peng, Y-K.M.; Perkins, L.J.; Spampinato, P.T.; Strickler, D.J.; Thomson, S.L.; Wagner, C.E.; Willms, R.S.; Reid, R.L.

    1988-04-01

    A tokamak systems code capable of modeling experimental test reactors has been developed and is described in this document. The code, named TETRA (for Tokamak Engineering Test Reactor Analysis), consists of a series of modules, each describing a tokamak system or component, controlled by an optimizer/driver. This code development was a national effort in that the modules were contributed by members of the fusion community and integrated into a code by the Fusion Engineering Design Center. The code has been checked out on the Cray computers at the National Magnetic Fusion Energy Computing Center and has satisfactorily simulated the Tokamak Ignition/Burn Experimental Reactor II (TIBER) design. A feature of this code is the ability to perform optimization studies through the use of a numerical software package, which iterates prescribed variables to satisfy a set of prescribed equations or constraints. This code will be used to perform sensitivity studies for the proposed International Thermonuclear Experimental Reactor (ITER). 22 figs., 29 tabs.

  7. Patients with Parkinson's Disease Learn to Control Complex Systems via Procedural as Well as Non-Procedural Learning

    ERIC Educational Resources Information Center

    Osman, Magda; Wilkinson, Leonora; Beigi, Mazda; Castaneda, Cristina Sanchez; Jahanshahi, Marjan

    2008-01-01

    The striatum is considered to mediate some forms of procedural learning. Complex dynamic control (CDC) tasks involve an individual having to make a series of sequential decisions to achieve a specific outcome (e.g. learning to operate and control a car), and they involve procedural learning. The aim of this study was to test the hypothesis that…

  8. Patients with Parkinson's Disease Learn to Control Complex Systems via Procedural as Well as Non-Procedural Learning

    ERIC Educational Resources Information Center

    Osman, Magda; Wilkinson, Leonora; Beigi, Mazda; Castaneda, Cristina Sanchez; Jahanshahi, Marjan

    2008-01-01

    The striatum is considered to mediate some forms of procedural learning. Complex dynamic control (CDC) tasks involve an individual having to make a series of sequential decisions to achieve a specific outcome (e.g. learning to operate and control a car), and they involve procedural learning. The aim of this study was to test the hypothesis that…

  9. Combustion Control of Diesel Engine using Feedback Error Learning with Kernel Online Learning Approach

    NASA Astrophysics Data System (ADS)

    Widayaka, Elfady Satya; Ohmori, Hiromitsu

    2016-09-01

    This paper shows how to design Multivariable Model Reference Adaptive Control System (MRACS) for “Tokyo University discrete-time engine model” proposed by Yasuda et al (2014). This controller configuration has the structure of “Feedback error learning (FEL)” and adaptive law is based on kernel method. Simulation results indicate that “kernelized” adaptive controllers can improve the tracking performance, the speed of convergence and the robustness to disturbances.

  10. Beamforming and power control in sensor arrays using reinforcement learning.

    PubMed

    Almeida, Náthalee C; Fernandes, Marcelo A C; Neto, Adrião D D

    2015-03-19

    The use of beamforming and power control, combined or separately, has advantages and disadvantages, depending on the application. The combined use of beamforming and power control has been shown to be highly effective in applications involving the suppression of interference signals from different sources. However, it is necessary to identify efficient methodologies for the combined operation of these two techniques. The most appropriate technique may be obtained by means of the implementation of an intelligent agent capable of making the best selection between beamforming and power control. The present paper proposes an algorithm using reinforcement learning (RL) to determine the optimal combination of beamforming and power control in sensor arrays. The RL algorithm used was Q-learning, employing an ε-greedy policy, and training was performed using the offline method. The simulations showed that RL was effective for implementation of a switching policy involving the different techniques, taking advantage of the positive characteristics of each technique in terms of signal reception.

  11. Language Learning and Control in Monolinguals and Bilinguals

    ERIC Educational Resources Information Center

    Bartolotti, James; Marian, Viorica

    2012-01-01

    Parallel language activation in bilinguals leads to competition between languages. Experience managing this interference may aid novel language learning by improving the ability to suppress competition from known languages. To investigate the effect of bilingualism on the ability to control native-language interference, monolinguals and bilinguals…

  12. Language Learning and Control in Monolinguals and Bilinguals

    ERIC Educational Resources Information Center

    Bartolotti, James; Marian, Viorica

    2012-01-01

    Parallel language activation in bilinguals leads to competition between languages. Experience managing this interference may aid novel language learning by improving the ability to suppress competition from known languages. To investigate the effect of bilingualism on the ability to control native-language interference, monolinguals and bilinguals…

  13. Learning and Adaptive Hybrid Systems for Nonlinear Control

    DTIC Science & Technology

    1991-05-01

    stimulating discussions. To Pete, Dino , Carole, and J.P.: thanks for making working at Draper fun, and for letting me learn something about what you were... extinction of the response in a manner which is also realistic. It has also been applied to control. Multiple Drive Reinforcement neurons have been

  14. Runaway electrons and ITER

    NASA Astrophysics Data System (ADS)

    Boozer, Allen H.

    2017-05-01

    The potential for damage, the magnitude of the extrapolation, and the importance of the atypical—incidents that occur once in a thousand shots—make theory and simulation essential for ensuring that relativistic runaway electrons will not prevent ITER from achieving its mission. Most of the theoretical literature on electron runaway assumes magnetic surfaces exist. ITER planning for the avoidance of halo and runaway currents is focused on massive-gas or shattered-pellet injection of impurities. In simulations of experiments, such injections lead to a rapid large-scale magnetic-surface breakup. Surface breakup, which is a magnetic reconnection, can occur on a quasi-ideal Alfvénic time scale when the resistance is sufficiently small. Nevertheless, the removal of the bulk of the poloidal flux, as in halo-current mitigation, is on a resistive time scale. The acceleration of electrons to relativistic energies requires the confinement of some tubes of magnetic flux within the plasma and a resistive time scale. The interpretation of experiments on existing tokamaks and their extrapolation to ITER should carefully distinguish confined versus unconfined magnetic field lines and quasi-ideal versus resistive evolution. The separation of quasi-ideal from resistive evolution is extremely challenging numerically, but is greatly simplified by constraints of Maxwell’s equations, and in particular those associated with magnetic helicity. The physics of electron runaway along confined magnetic field lines is clarified by relations among the poloidal flux change required for an e-fold in the number of electrons, the energy distribution of the relativistic electrons, and the number of relativistic electron strikes that can be expected in a single disruption event.

  15. Nonlinear Performance Seeking Control using Fuzzy Model Reference Learning Control and the Method of Steepest Descent

    NASA Technical Reports Server (NTRS)

    Kopasakis, George

    1997-01-01

    Performance Seeking Control (PSC) attempts to find and control the process at the operating condition that will generate maximum performance. In this paper a nonlinear multivariable PSC methodology will be developed, utilizing the Fuzzy Model Reference Learning Control (FMRLC) and the method of Steepest Descent or Gradient (SDG). This PSC control methodology employs the SDG method to find the operating condition that will generate maximum performance. This operating condition is in turn passed to the FMRLC controller as a set point for the control of the process. The conventional SDG algorithm is modified in this paper in order for convergence to occur monotonically. For the FMRLC control, the conventional fuzzy model reference learning control methodology is utilized, with guidelines generated here for effective tuning of the FMRLC controller.

  16. Iterative Magnetometer Calibration

    NASA Technical Reports Server (NTRS)

    Sedlak, Joseph

    2006-01-01

    This paper presents an iterative method for three-axis magnetometer (TAM) calibration that makes use of three existing utilities recently incorporated into the attitude ground support system used at NASA's Goddard Space Flight Center. The method combines attitude-independent and attitude-dependent calibration algorithms with a new spinning spacecraft Kalman filter to solve for biases, scale factors, nonorthogonal corrections to the alignment, and the orthogonal sensor alignment. The method is particularly well-suited to spin-stabilized spacecraft, but may also be useful for three-axis stabilized missions given sufficient data to provide observability.

  17. Searching with iterated maps

    PubMed Central

    Elser, V.; Rankenburg, I.; Thibault, P.

    2007-01-01

    In many problems that require extensive searching, the solution can be described as satisfying two competing constraints, where satisfying each independently does not pose a challenge. As an alternative to tree-based and stochastic searching, for these problems we propose using an iterated map built from the projections to the two constraint sets. Algorithms of this kind have been the method of choice in a large variety of signal-processing applications; we show here that the scope of these algorithms is surprisingly broad, with applications as diverse as protein folding and Sudoku. PMID:17202267

  18. Multi Car Elevator Control by using Learning Automaton

    NASA Astrophysics Data System (ADS)

    Shiraishi, Kazuaki; Hamagami, Tomoki; Hirata, Hironori

    We study an adaptive control technique for multi car elevators (MCEs) by adopting learning automatons (LAs.) The MCE is a high performance and a near-future elevator system with multi shafts and multi cars. A strong point of the system is that realizing a large carrying capacity in small shaft area. However, since the operation is too complicated, realizing an efficient MCE control is difficult for top-down approaches. For example, “bunching up together" is one of the typical phenomenon in a simple traffic environment like the MCE. Furthermore, an adapting to varying environment in configuration requirement is a serious issue in a real elevator service. In order to resolve these issues, having an autonomous behavior is required to the control system of each car in MCE system, so that the learning automaton, as the solutions for this requirement, is supposed to be appropriate for the simple traffic control. First, we assign a stochastic automaton (SA) to each car control system. Then, each SA varies its stochastic behavior distributions for adapting to environment in which its policy is evaluated with each passenger waiting times. That is LA which learns the environment autonomously. Using the LA based control technique, the MCE operation efficiency is evaluated through simulation experiments. Results show the technique enables reducing waiting times efficiently, and we confirm the system can adapt to the dynamic environment.

  19. Attention control learning in the decision space using state estimation

    NASA Astrophysics Data System (ADS)

    Gharaee, Zahra; Fatehi, Alireza; Mirian, Maryam S.; Nili Ahmadabadi, Majid

    2016-05-01

    The main goal of this paper is modelling attention while using it in efficient path planning of mobile robots. The key challenge in concurrently aiming these two goals is how to make an optimal, or near-optimal, decision in spite of time and processing power limitations, which inherently exist in a typical multi-sensor real-world robotic application. To efficiently recognise the environment under these two limitations, attention of an intelligent agent is controlled by employing the reinforcement learning framework. We propose an estimation method using estimated mixture-of-experts task and attention learning in perceptual space. An agent learns how to employ its sensory resources, and when to stop observing, by estimating its perceptual space. In this paper, static estimation of the state space in a learning task problem, which is examined in the WebotsTM simulator, is performed. Simulation results show that a robot learns how to achieve an optimal policy with a controlled cost by estimating the state space instead of continually updating sensory information.

  20. Human-level control through deep reinforcement learning.

    PubMed

    Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Rusu, Andrei A; Veness, Joel; Bellemare, Marc G; Graves, Alex; Riedmiller, Martin; Fidjeland, Andreas K; Ostrovski, Georg; Petersen, Stig; Beattie, Charles; Sadik, Amir; Antonoglou, Ioannis; King, Helen; Kumaran, Dharshan; Wierstra, Daan; Legg, Shane; Hassabis, Demis

    2015-02-26

    The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

  1. Human-level control through deep reinforcement learning

    NASA Astrophysics Data System (ADS)

    Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Rusu, Andrei A.; Veness, Joel; Bellemare, Marc G.; Graves, Alex; Riedmiller, Martin; Fidjeland, Andreas K.; Ostrovski, Georg; Petersen, Stig; Beattie, Charles; Sadik, Amir; Antonoglou, Ioannis; King, Helen; Kumaran, Dharshan; Wierstra, Daan; Legg, Shane; Hassabis, Demis

    2015-02-01

    The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

  2. [Lessons learned from tobacco control in Spain].

    PubMed

    Fernández, Esteve; Villalbí, Joan R; Córdoba, Rodrigo

    2006-01-01

    The growing involvement in Spain by civil society in the demand for tobacco control policies has been notable. The basis for the creation of the National Committee for Tobacco Prevention was established in 2004. At the end of that year, an intensive intervention was aimed at specifying, in law, the regulatory actions in the National Plan for Tobacco Prevention. This would facilitate a qualitative leap, taking advantage of the legal transposition of the European directive on advertising. With broad political consensus, the Law 28/2005 was established regarding sanitary measures for tobacco and the regulation of the sale, supply and consumption of tobacco products. The objective stated in this law is to prevent the initiation of tobacco consumption, especially among youth, guarantee the right of non-smokers to breathe air free from tobacco smoke and make quitting this habit easier for people who wish to do so. The main issues included are the prohibition of tobacco advertising and the limitation of tobacco consumption in common work areas and enclosed public spaces. The new law has replaced the previous rules in Spain, which were some of the most permissive in the European Union in terms of tobacco sales, advertising limitations and restrictions on smoking locations. It is clear that there is still much to be done. At this time, more social support needs to be generated in favor of the new regulations, and an important effort needs to be made to educate the public.

  3. PREFACE: Progress in the ITER Physics Basis

    NASA Astrophysics Data System (ADS)

    Ikeda, K.

    2007-06-01

    fundamental to its completion. I am pleased to witness the extensive collaborations, the excellent working relationships and the free exchange of views that have been developed among scientists working on magnetic fusion, and I would particularly like to acknowledge the importance which they assign to ITER in their research. This close collaboration and the spirit of free discussion will be essential to the success of ITER. Finally, the PIPB identifies issues which remain in the projection of burning plasma performance to the ITER scale and in the control of burning plasmas. Continued R&D is therefore called for to reduce the uncertainties associated with these issues and to ensure the efficient operation and exploitation of ITER. It is important that the international fusion community maintains a high level of collaboration in the future to address these issues and to prepare the physics basis for ITER operation. ITPA Coordination Committee R. Stambaugh (Chair of ITPA CC, General Atomics, USA) D.J. Campbell (Previous Chair of ITPA CC, European Fusion Development Agreement—Close Support Unit, ITER Organization) M. Shimada (Co-Chair of ITPA CC, ITER Organization) R. Aymar (ITER International Team, CERN) V. Chuyanov (ITER Organization) J.H. Han (Korea Basic Science Institute, Korea) Y. Huo (Zengzhou University, China) Y.S. Hwang (Seoul National University, Korea) N. Ivanov (Kurchatov Institute, Russia) Y. Kamada (Japan Atomic Energy Agency, Naka, Japan) P.K. Kaw (Institute for Plasma Research, India) S. Konovalov (Kurchatov Institute, Russia) M. Kwon (National Fusion Research Center, Korea) J. Li (Academy of Science, Institute of Plasma Physics, China) S. Mirnov (TRINITI, Russia) Y. Nakamura (National Institute for Fusion Studies, Japan) H. Ninomiya (Japan Atomic Energy Agency, Naka, Japan) E. Oktay (Department of Energy, USA) J. Pamela (European Fusion Development Agreement—Close Support Unit) C. Pan (Southwestern Institute of Physics, China) F. Romanelli (Ente per le

  4. Self-controlled practice benefits motor learning in older adults.

    PubMed

    Lessa, Helena Thofehrn; Chiviacowsky, Suzete

    2015-04-01

    Providing learners with the chance to choose over certain aspects of practice has been consistently shown to facilitate the acquisition of motor skills in several populations. However, studies investigating the effects of providing autonomy support during the learning process of older adults remain scarce. The objective of the present study was to investigate the effects of self-controlled amount of practice on the learning of a sequential motor task in older adults. Participants in the self-control group were able to choose when to stop practicing a speed cup stacking task, while the number of practice trials for a yoked group was pre-determined, mirroring the self-control group. The opportunity to choose when stop practicing facilitated motor performance and learning compared to the yoked condition. The findings suggest that letting older adult learners choose the amount of practice, supporting their autonomy needs, has a positive influence on motor learning. Copyright © 2015 Elsevier B.V. All rights reserved.

  5. ITER helium ash accumulation

    SciTech Connect

    Hogan, J.T.; Hillis, D.L.; Galambos, J.; Uckan, N.A. ); Dippel, K.H.; Finken, K.H. . Inst. fuer Plasmaphysik); Hulse, R.A.; Budny, R.V. . Plasma Physics Lab.)

    1990-01-01

    Many studies have shown the importance of the ratio {upsilon}{sub He}/{upsilon}{sub E} in determining the level of He ash accumulation in future reactor systems. Results of the first tokamak He removal experiments have been analysed, and a first estimate of the ratio {upsilon}{sub He}/{upsilon}{sub E} to be expected for future reactor systems has been made. The experiments were carried out for neutral beam heated plasmas in the TEXTOR tokamak, at KFA/Julich. Helium was injected both as a short puff and continuously, and subsequently extracted with the Advanced Limiter Test-II pump limiter. The rate at which the He density decays has been determined with absolutely calibrated charge exchange spectroscopy, and compared with theoretical models, using the Multiple Impurity Species Transport (MIST) code. An analysis of energy confinement has been made with PPPL TRANSP code, to distinguish beam from thermal confinement, especially for low density cases. The ALT-II pump limiter system is found to exhaust the He with maximum exhaust efficiency (8 pumps) of {approximately}8%. We find 1<{upsilon}{sub He}/{upsilon}{sub E}<3.3 for the database of cases analysed to date. Analysis with the ITER TETRA systems code shows that these values would be adequate to achieve the required He concentration with the present ITER divertor He extraction system.

  6. Runaway electrons and ITER

    NASA Astrophysics Data System (ADS)

    Boozer, Allen

    2016-10-01

    ITER planning for avoiding runaway damage depends on magnetic surface breakup in fast relaxations. These arise in thermal quenches and in the spreading of impurities from massive gas injection or shattered pellets. Surface breakup would prevent a runaway to relativistic energies were it not for non-intercepting flux tubes, which contain magnetic field lines that do not intercept the walls. Such tubes persist near the magnetic axis and in the cores of islands but must dissipate before any confining surfaces re-form. Otherwise, a highly dangerous situation arises. Electrons that were trapped and accelerated in these flux tubes can fill a large volume of stochastic field lines and serve as a seed for the transfer of the full plasma current to runaways. If the outer confining surfaces are punctured, as by a drift into the wall, then the full runaway inventory will be lost in a short pulse along a narrow flux tube. Although not part of ITER planning, currents induced in the walls by the fast magnetic relaxation could be used to passively prevent outer surfaces re-forming. If magnetic surface breakup can be avoided during impurity injection, the plasma current could be terminated in tens of milliseconds by plasma cooling with no danger of runaway. Support by DoE Office of Fusion Energy Science Grant De-FG02-03ER54696.

  7. Neural Network-Based Learning Impedance Control for a Robot

    NASA Astrophysics Data System (ADS)

    Xiao, Nan-Feng; Todo, Isao

    In this paper, a neural network-based learning approach for robot impedance control is presented to accomplish a contact task. Firstly, a discrete-time impedance control algorithm is obtained to control the contact task of the robot. Secondly, on-line learning algorithms based on a new evaluation function are developed for the neural networks which adjust the inertia, damping and stiffness parameters of the robot in order to adapt it to the unknown contact environment. Thirdly, experiments are carried out and the effecttiveness of the present approach is verified by pressing a spring using a 6 degrees of freedom robot. The adaptiveness, stability and flexibility of the present approach are also confirmed by the experimental ersults.

  8. Learning and Control Model of the Arm for Loading

    NASA Astrophysics Data System (ADS)

    Kim, Kyoungsik; Kambara, Hiroyuki; Shin, Duk; Koike, Yasuharu

    We propose a learning and control model of the arm for a loading task in which an object is loaded onto one hand with the other hand, in the sagittal plane. Postural control during object interactions provides important points to motor control theories in terms of how humans handle dynamics changes and use the information of prediction and sensory feedback. For the learning and control model, we coupled a feedback-error-learning scheme with an Actor-Critic method used as a feedback controller. To overcome sensory delays, a feedforward dynamics model (FDM) was used in the sensory feedback path. We tested the proposed model in simulation using a two-joint arm with six muscles, each with time delays in muscle force generation. By applying the proposed model to the loading task, we showed that motor commands started increasing, before an object was loaded on, to stabilize arm posture. We also found that the FDM contributes to the stabilization by predicting how the hand changes based on contexts of the object and efferent signals. For comparison with other computational models, we present the simulation results of a minimum-variance model.

  9. Overview on Experiments On ITER-like Antenna On JET And ICRF Antenna Design For ITER

    SciTech Connect

    Nightingale, M. P. S.; Blackman, T.; Edwards, D.; Fanthome, J.; Graham, M.; Hamlyn-Harris, C.; Hancock, D.; Jacquet, P.; Mayoral, M.-L.; Monakhov, I.; Nicholls, K.; Stork, D.; Whitehurst, A.; Wilson, D.; Wooldridge, E.

    2009-11-26

    Following an overview of the ITER Ion Cyclotron Resonance Frequency (ICRF) system, the JET ITER-like antenna (ILA) will be described. The ILA was designed to test the following ITER issues: (a) reliable operation at power densities of order 8 MW/m{sup 2} at voltages up to 45 kV using a close-packed array of straps; (b) powering through ELMs using an internal (in-vacuum) conjugate-T junction; (c) protection from arcing in a conjugate-T configuration, using both existing and novel systems; and (d) resilience to disruption forces. ITER-relevant results have been achieved: operation at high coupled power density; control of the antenna matching elements in the presence of high inter-strap coupling, use of four conjugate-T systems (as would be used in ITER, should a conjugate-T approach be used); operation with RF voltages on the antenna structures up to 42 kV; achievement of ELM tolerance with a conjugate-T configuration by operating at 3{omega} real impedance at the conjugate-T point; and validation of arc detection systems on conjugate-T configurations in ELMy H-mode plasmas. The impact of these results on the predicted performance and design of the ITER antenna will be reviewed. In particular, the implications of the RF coupling measured on JET will be discussed.

  10. The use of control groups in artificial grammar learning.

    PubMed

    Reber, Rolf; Perruchet, Pierre

    2003-01-01

    Experimenters assume that participants of an experimental group have learned an artificial grammar if they classify test items with significantly higher accuracy than does a control group without training. The validity of such a comparison, however, depends on an additivity assumption: Learning is superimposed on the action of non-specific variables-for example, repetitions of letters, which modulate the performance of the experimental group and the control group to the same extent. In two experiments we were able to show that this additivity assumption does not hold. Grammaticality classifications in control groups without training (Experiments 1 and 2) depended on non-specific features. There were no such biases in the experimental groups. Control groups with training on randomized strings (Experiment 2) showed fewer biases than did control groups without training. Furthermore, we reanalysed published research and demonstrated that earlier experiments using control groups without training had produced similar biases in control group performances, bolstering the finding that using control groups without training is methodologically unsound.

  11. Design and Control of Large Collections of Learning Agents

    NASA Technical Reports Server (NTRS)

    Agogino, Adrian

    2001-01-01

    The intelligent control of multiple autonomous agents is an important yet difficult task. Previous methods used to address this problem have proved to be either too brittle, too hard to use, or not scalable to large systems. The 'Collective Intelligence' project at NASA/Ames provides an elegant, machine-learning approach to address these problems. This approach mathematically defines some essential properties that a reward system should have to promote coordinated behavior among reinforcement learners. This work has focused on creating additional key properties and algorithms within the mathematics of the Collective Intelligence framework. One of the additions will allow agents to learn more quickly, in a more coordinated manner. The other will let agents learn with less knowledge of their environment. These additions will allow the framework to be applied more easily, to a much larger domain of multi-agent problems.

  12. The Effectiveness of E-Learning Systems: A Review of the Empirical Literature on Learner Control

    ERIC Educational Resources Information Center

    Sorgenfrei, Christian; Smolnik, Stefan

    2016-01-01

    E-learning systems are considerably changing education and organizational training. With the advancement of online-based learning systems, learner control over the instructional process has emerged as a decisive factor in technology-based forms of learning. However, conceptual work on the role of learner control in e-learning has not advanced…

  13. The Effectiveness of E-Learning Systems: A Review of the Empirical Literature on Learner Control

    ERIC Educational Resources Information Center

    Sorgenfrei, Christian; Smolnik, Stefan

    2016-01-01

    E-learning systems are considerably changing education and organizational training. With the advancement of online-based learning systems, learner control over the instructional process has emerged as a decisive factor in technology-based forms of learning. However, conceptual work on the role of learner control in e-learning has not advanced…

  14. Learning to Learn Online: Using Locus of Control to Help Students Become Successful Online Learners

    ERIC Educational Resources Information Center

    Lowes, Susan; Lin, Peiyi

    2015-01-01

    In this study, approximately 600 online high school students were asked to take Rotter's locus of control questionnaire and then reflect on the results, with the goal of helping them think about their ability to regulate their learning in this new environment. In addition, it was hoped that the results could provide a diagnostic for teachers who…

  15. Preliminary consideration of CFETR ITER-like case diagnostic system

    SciTech Connect

    Li, G. S.; Liu, Y. K.; Gao, X.; Yang, Y. Wang, Y. M.; Ming, T. F.; Han, X.; Liu, S. C.; Wang, E. H.; Yang, W. J.; Li, G. Q.; Hu, Q. S.

    2016-11-15

    Chinese Fusion Engineering Test Reactor (CFETR) is a new superconducting tokamak device being designed in China, which aims at bridging the gap between ITER and DEMO, where DEMO is a tokamak demonstration fusion reactor. Two diagnostic cases, ITER-like case and towards DEMO case, have been considered for CFETR early and later operating phases, respectively. In this paper, some preliminary consideration of ITER-like case will be presented. Based on ITER diagnostic system, three versions of increased complexity and coverage of the ITER-like case diagnostic system have been developed with different goals and functions. Version A aims only machine protection and basic control. Both of version B and version C are mainly for machine protection, basic and advanced control, but version C has an increased level of redundancy necessary for improved measurements capability. The performance of these versions and needed R&D work are outlined.

  16. Preliminary consideration of CFETR ITER-like case diagnostic system

    NASA Astrophysics Data System (ADS)

    Li, G. S.; Yang, Y.; Wang, Y. M.; Ming, T. F.; Han, X.; Liu, S. C.; Wang, E. H.; Liu, Y. K.; Yang, W. J.; Li, G. Q.; Hu, Q. S.; Gao, X.

    2016-11-01

    Chinese Fusion Engineering Test Reactor (CFETR) is a new superconducting tokamak device being designed in China, which aims at bridging the gap between ITER and DEMO, where DEMO is a tokamak demonstration fusion reactor. Two diagnostic cases, ITER-like case and towards DEMO case, have been considered for CFETR early and later operating phases, respectively. In this paper, some preliminary consideration of ITER-like case will be presented. Based on ITER diagnostic system, three versions of increased complexity and coverage of the ITER-like case diagnostic system have been developed with different goals and functions. Version A aims only machine protection and basic control. Both of version B and version C are mainly for machine protection, basic and advanced control, but version C has an increased level of redundancy necessary for improved measurements capability. The performance of these versions and needed R&D work are outlined.

  17. Preliminary consideration of CFETR ITER-like case diagnostic system.

    PubMed

    Li, G S; Yang, Y; Wang, Y M; Ming, T F; Han, X; Liu, S C; Wang, E H; Liu, Y K; Yang, W J; Li, G Q; Hu, Q S; Gao, X

    2016-11-01

    Chinese Fusion Engineering Test Reactor (CFETR) is a new superconducting tokamak device being designed in China, which aims at bridging the gap between ITER and DEMO, where DEMO is a tokamak demonstration fusion reactor. Two diagnostic cases, ITER-like case and towards DEMO case, have been considered for CFETR early and later operating phases, respectively. In this paper, some preliminary consideration of ITER-like case will be presented. Based on ITER diagnostic system, three versions of increased complexity and coverage of the ITER-like case diagnostic system have been developed with different goals and functions. Version A aims only machine protection and basic control. Both of version B and version C are mainly for machine protection, basic and advanced control, but version C has an increased level of redundancy necessary for improved measurements capability. The performance of these versions and needed R&D work are outlined.

  18. Effects of Locus of Control and Learner-Control on Web-Based Language Learning

    ERIC Educational Resources Information Center

    Chang, Mei-Mei; Ho, Chiung-Mei

    2009-01-01

    The study explored the effects of students' locus of control and types of control over instruction on their self-efficacy and performance in a web-based language learning environment. A web-based interactive instructional program focusing on the comprehension of news articles for English language learners was developed in two versions: learner-…

  19. Effects of Locus of Control and Learner-Control on Web-Based Language Learning

    ERIC Educational Resources Information Center

    Chang, Mei-Mei; Ho, Chiung-Mei

    2009-01-01

    The study explored the effects of students' locus of control and types of control over instruction on their self-efficacy and performance in a web-based language learning environment. A web-based interactive instructional program focusing on the comprehension of news articles for English language learners was developed in two versions: learner-…

  20. Design issues of a reinforcement-based self-learning fuzzy controller for petrochemical process control

    NASA Technical Reports Server (NTRS)

    Yen, John; Wang, Haojin; Daugherity, Walter C.

    1992-01-01

    Fuzzy logic controllers have some often-cited advantages over conventional techniques such as PID control, including easier implementation, accommodation to natural language, and the ability to cover a wider range of operating conditions. One major obstacle that hinders the broader application of fuzzy logic controllers is the lack of a systematic way to develop and modify their rules; as a result the creation and modification of fuzzy rules often depends on trial and error or pure experimentation. One of the proposed approaches to address this issue is a self-learning fuzzy logic controller (SFLC) that uses reinforcement learning techniques to learn the desirability of states and to adjust the consequent part of its fuzzy control rules accordingly. Due to the different dynamics of the controlled processes, the performance of a self-learning fuzzy controller is highly contingent on its design. The design issue has not received sufficient attention. The issues related to the design of a SFLC for application to a petrochemical process are discussed, and its performance is compared with that of a PID and a self-tuning fuzzy logic controller.

  1. Extortion can outperform generosity in the iterated prisoner's dilemma

    PubMed Central

    Wang, Zhijian; Zhou, Yanran; Lien, Jaimie W.; Zheng, Jie; Xu, Bin

    2016-01-01

    Zero-determinant (ZD) strategies, as discovered by Press and Dyson, can enforce a linear relationship between a pair of players' scores in the iterated prisoner's dilemma. Particularly, the extortionate ZD strategies can enforce and exploit cooperation, providing a player with a score advantage, and consequently higher scores than those from either mutual cooperation or generous ZD strategies. In laboratory experiments in which human subjects were paired with computer co-players, we demonstrate that both the generous and the extortionate ZD strategies indeed enforce a unilateral control of the reward. When the experimental setting is sufficiently long and the computerized nature of the opponent is known to human subjects, the extortionate strategy outperforms the generous strategy. Human subjects' cooperation rates when playing against extortionate and generous ZD strategies are similar after learning has occurred. More than half of extortionate strategists finally obtain an average score higher than that from mutual cooperation. PMID:27067513

  2. Extortion can outperform generosity in the iterated prisoner's dilemma.

    PubMed

    Wang, Zhijian; Zhou, Yanran; Lien, Jaimie W; Zheng, Jie; Xu, Bin

    2016-04-12

    Zero-determinant (ZD) strategies, as discovered by Press and Dyson, can enforce a linear relationship between a pair of players' scores in the iterated prisoner's dilemma. Particularly, the extortionate ZD strategies can enforce and exploit cooperation, providing a player with a score advantage, and consequently higher scores than those from either mutual cooperation or generous ZD strategies. In laboratory experiments in which human subjects were paired with computer co-players, we demonstrate that both the generous and the extortionate ZD strategies indeed enforce a unilateral control of the reward. When the experimental setting is sufficiently long and the computerized nature of the opponent is known to human subjects, the extortionate strategy outperforms the generous strategy. Human subjects' cooperation rates when playing against extortionate and generous ZD strategies are similar after learning has occurred. More than half of extortionate strategists finally obtain an average score higher than that from mutual cooperation.

  3. ITER shielding blanket

    SciTech Connect

    Strebkov, Y.; Blinov, Y.; Avsjannikov, A.

    1994-12-31

    A version of ITER shielding blanket design is presented. Main features of this proposal are: Cu based alloy as structure material of the first wall - integrated in the blanket segment box structure and 316L SS as material of both back/side walls of the box and the shield structure elements; water of medium pressure (up to 4 MPa) as coolant with toroidal direction of flow; two variants of beryllium protection tiles joining (either permanent joints by mince of solid diffusion bonding or demountable attachment with compliant layer; for last versions designs options of tiles attachment units are given). Problems of manufacturing of such blanket segment including its assembly sequence are considered in details. Results of stress problem analysis for thermal, pressure, and electromagnetic loads will be given in this report also.

  4. Iterated crowdsourcing dilemma game

    PubMed Central

    Oishi, Koji; Cebrian, Manuel; Abeliuk, Andres; Masuda, Naoki

    2014-01-01

    The Internet has enabled the emergence of collective problem solving, also known as crowdsourcing, as a viable option for solving complex tasks. However, the openness of crowdsourcing presents a challenge because solutions obtained by it can be sabotaged, stolen, and manipulated at a low cost for the attacker. We extend a previously proposed crowdsourcing dilemma game to an iterated game to address this question. We enumerate pure evolutionarily stable strategies within the class of so-called reactive strategies, i.e., those depending on the last action of the opponent. Among the 4096 possible reactive strategies, we find 16 strategies each of which is stable in some parameter regions. Repeated encounters of the players can improve social welfare when the damage inflicted by an attack and the cost of attack are both small. Under the current framework, repeated interactions do not really ameliorate the crowdsourcing dilemma in a majority of the parameter space. PMID:24526244

  5. Iterated crowdsourcing dilemma game

    NASA Astrophysics Data System (ADS)

    Oishi, Koji; Cebrian, Manuel; Abeliuk, Andres; Masuda, Naoki

    2014-02-01

    The Internet has enabled the emergence of collective problem solving, also known as crowdsourcing, as a viable option for solving complex tasks. However, the openness of crowdsourcing presents a challenge because solutions obtained by it can be sabotaged, stolen, and manipulated at a low cost for the attacker. We extend a previously proposed crowdsourcing dilemma game to an iterated game to address this question. We enumerate pure evolutionarily stable strategies within the class of so-called reactive strategies, i.e., those depending on the last action of the opponent. Among the 4096 possible reactive strategies, we find 16 strategies each of which is stable in some parameter regions. Repeated encounters of the players can improve social welfare when the damage inflicted by an attack and the cost of attack are both small. Under the current framework, repeated interactions do not really ameliorate the crowdsourcing dilemma in a majority of the parameter space.

  6. Hierarchical control of procedural and declarative category-learning systems.

    PubMed

    Turner, Benjamin O; Crossley, Matthew J; Ashby, F Gregory

    2017-04-15

    Substantial evidence suggests that human category learning is governed by the interaction of multiple qualitatively distinct neural systems. In this view, procedural memory is used to learn stimulus-response associations, and declarative memory is used to apply explicit rules and test hypotheses about category membership. However, much less is known about the interaction between these systems: how is control passed between systems as they interact to influence motor resources? Here, we used fMRI to elucidate the neural correlates of switching between procedural and declarative categorization systems. We identified a key region of the cerebellum (left Crus I) whose activity was bidirectionally modulated depending on switch direction. We also identified regions of the default mode network (DMN) that were selectively connected to left Crus I during switching. We propose that the cerebellum-in coordination with the DMN-serves a critical role in passing control between procedural and declarative memory systems.

  7. Emotional Learning Based Intelligent Controllers for Rotor Flux Oriented Control of Induction Motor

    NASA Astrophysics Data System (ADS)

    Abdollahi, Rohollah; Farhangi, Reza; Yarahmadi, Ali

    2014-08-01

    This paper presents design and evaluation of a novel approach based on emotional learning to improve the speed control system of rotor flux oriented control of induction motor. The controller includes a neuro-fuzzy system with speed error and its derivative as inputs. A fuzzy critic evaluates the present situation, and provides the emotional signal (stress). The controller modifies its characteristics so that the critics stress is reduced. The comparative simulation results show that the proposed controller is more robust and hence found to be a suitable replacement of the conventional PI controller for the high performance industrial drive applications.

  8. Exploratory learning with a computer simulation for control theory: Learning processes and instructional support

    NASA Astrophysics Data System (ADS)

    Njoo, Melanie; de Jong, Ton

    Computer simulations create a context that is well fitted for exploratory or discovery learning. The aim of the present two studies was to gain deeper insight into what constitutes exploratory learning and to assess the effects of a number of instructional support measures. The domain involved was control theory at the university level. In the first study we made an inventory of exploratory learning processes by observing 17 students working with a computer simulation and analyzing students' thinking-aloud protocols. Subjects received a structured assignment with hints as an instructional support measure. In the second study, 91 students received an open-ended assignment with instructional support that consisted of an information sheet and a set of fill-in forms. On both sheets and forms, six cells were presented. A cell was given for each of the following six learning processes: identifying variables and parameters, generating hypotheses, designing an experiment, predicting, interpreting data, and drawing of conclusions. Information sheets were either of a domain specific or of a general nature. The set of fill-in forms were either free or had the cell HYPOTHESIS already filled in. The statements of the students on the fill-in forms were analyzed in a stepwise order. Twenty-two detailed learning processes were identified and classified. Two of the main classes of processes are transformative and regulative. Both studies showed that students were reluctant to apply learning processes that are considered characteristic for exploratory learning. Furthermore, students had problems with the exploratory learning processes, especially with the processes of generating hypotheses, interpreting data, and drawing conclusions. Effects of the instructional support measures were not conclusive. Hints did not result in significant improvements of the study process. Supporting learning processes with information sheets appeared to help students in performing learning processes

  9. Self-learning fuzzy controllers based on temporal back propagation

    NASA Technical Reports Server (NTRS)

    Jang, Jyh-Shing R.

    1992-01-01

    This paper presents a generalized control strategy that enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near-optimal manner. This methodology, termed temporal back propagation, is model-insensitive in the sense that it can deal with plants that can be represented in a piecewise-differentiable format, such as difference equations, neural networks, GMDH structures, and fuzzy models. Regardless of the numbers of inputs and outputs of the plants under consideration, the proposed approach can either refine the fuzzy if-then rules if human experts, or automatically derive the fuzzy if-then rules obtained from human experts are not available. The inverted pendulum system is employed as a test-bed to demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired fuzzy controller.

  10. Tuning fuzzy PD and PI controllers using reinforcement learning.

    PubMed

    Boubertakh, Hamid; Tadjine, Mohamed; Glorennec, Pierre-Yves; Labiod, Salim

    2010-10-01

    In this paper, we propose a new auto-tuning fuzzy PD and PI controllers using reinforcement Q-learning (QL) algorithm for SISO (single-input single-output) and TITO (two-input two-output) systems. We first, investigate the design parameters and settings of a typical class of Fuzzy PD (FPD) and Fuzzy PI (FPI) controllers: zero-order Takagi-Sugeno controllers with equidistant triangular membership functions for inputs, equidistant singleton membership functions for output, Larsen's implication method, and average sum defuzzification method. Secondly, the analytical structures of these typical fuzzy PD and PI controllers are compared to their classical counterpart PD and PI controllers. Finally, the effectiveness of the proposed method is proven through simulation examples.

  11. Force and position control of robot manipulators: Learning and repetitive control approach

    NASA Astrophysics Data System (ADS)

    Jeon, Doyoung

    When a robot performs the same task repeatedly, a learning or repetitive controller can enhance the performance of the system significantly. Learning or repetitive control, however, has not been studied in the force control of a robot manipulator as extensively as in the position control of a robot manipulator. In this dissertation, learning control is applied to hybrid force and position control of robot manipulators. Also, repetitive control is applied to the control of a spring loaded end effector. When the geometry and position of the constraint surface is known, the hybrid force and position controller and the feedforward compensator can be designed in the constraint coordinates. When the operation is periodic, the learning hybrid force and position control enhances the control performance as the feedforward compensator is updated in each cycle by the force and position error in the preceding trials. This scheme is proved to be stable in the sense of Lyapunov. In the experiments, a two degree of freedom SCARA-type direct-drive robot manipulator is used to test the feasibility of the learning hybrid force and position control. The deburring tool mounted on the upper link of the robot could follow a flat, tilted flat, and curved 1/4 inch aluminum plate with a desired contact force of 10 N (within the robot-mean-square force error of 1.95 N) and with desired tangential velocity. Considering the loss of contact observed at the initial trial, the performance of the system improved significantly. A spring loaded end effector is useful in assembly operations such as mounting an electronics package on a board. With the known stiffness of the spring, the desired contact force tracking is accomplished by controlling the spring displacement. Uncertainties in the environment such as the stiffness of the board make it difficult to program the required control force. As the operation repeats, the tracking errors for the spring displacement, i.e., the contact force, are

  12. Reinforcement learning techniques for controlling resources in power networks

    NASA Astrophysics Data System (ADS)

    Kowli, Anupama Sunil

    As power grids transition towards increased reliance on renewable generation, energy storage and demand response resources, an effective control architecture is required to harness the full functionalities of these resources. There is a critical need for control techniques that recognize the unique characteristics of the different resources and exploit the flexibility afforded by them to provide ancillary services to the grid. The work presented in this dissertation addresses these needs. Specifically, new algorithms are proposed, which allow control synthesis in settings wherein the precise distribution of the uncertainty and its temporal statistics are not known. These algorithms are based on recent developments in Markov decision theory, approximate dynamic programming and reinforcement learning. They impose minimal assumptions on the system model and allow the control to be "learned" based on the actual dynamics of the system. Furthermore, they can accommodate complex constraints such as capacity and ramping limits on generation resources, state-of-charge constraints on storage resources, comfort-related limitations on demand response resources and power flow limits on transmission lines. Numerical studies demonstrating applications of these algorithms to practical control problems in power systems are discussed. Results demonstrate how the proposed control algorithms can be used to improve the performance and reduce the computational complexity of the economic dispatch mechanism in a power network. We argue that the proposed algorithms are eminently suitable to develop operational decision-making tools for large power grids with many resources and many sources of uncertainty.

  13. Biomimetic Hybrid Feedback Feedforward Neural-Network Learning Control.

    PubMed

    Pan, Yongping; Yu, Haoyong

    2017-06-01

    This brief presents a biomimetic hybrid feedback feedforward neural-network learning control (NNLC) strategy inspired by the human motor learning control mechanism for a class of uncertain nonlinear systems. The control structure includes a proportional-derivative controller acting as a feedback servo machine and a radial-basis-function (RBF) NN acting as a feedforward predictive machine. Under the sufficient constraints on control parameters, the closed-loop system achieves semiglobal practical exponential stability, such that an accurate NN approximation is guaranteed in a local region along recurrent reference trajectories. Compared with the existing NNLC methods, the novelties of the proposed method include: 1) the implementation of an adaptive NN control to guarantee plant states being recurrent is not needed, since recurrent reference signals rather than plant states are utilized as NN inputs, which greatly simplifies the analysis and synthesis of the NNLC and 2) the domain of NN approximation can be determined a priori by the given reference signals, which leads to an easy construction of the RBF-NNs. Simulation results have verified the effectiveness of this approach.

  14. Biomimetic Hybrid Feedback Feedforward Neural-Network Learning Control.

    PubMed

    Pan, Yongping; Yu, Haoyong

    2016-03-30

    This brief presents a biomimetic hybrid feedback feedforward neural-network learning control (NNLC) strategy inspired by the human motor learning control mechanism for a class of uncertain nonlinear systems. The control structure includes a proportional-derivative controller acting as a feedback servo machine and a radial-basis-function (RBF) NN acting as a feedforward predictive machine. Under the sufficient constraints on control parameters, the closed-loop system achieves semiglobal practical exponential stability, such that an accurate NN approximation is guaranteed in a local region along recurrent reference traj- ectories. Compared with the existing NNLC methods, the novelties of the proposed method include: 1) the implementation of an adaptive NN control to guarantee plant states being recurrent is not needed, since recurrent reference signals rather than plant states are utilized as NN inputs, which greatly simplifies the analysis and synthesis of the NNLC and 2) the domain of NN approximation can be determined a priori by the given reference signals, which leads to an easy construction of the RBF-NNs. Simulation results have verified the effectiveness of this approach.

  15. Intelligent control of an IPMC actuated manipulator using emotional learning-based controller

    NASA Astrophysics Data System (ADS)

    Shariati, Azadeh; Meghdari, Ali; Shariati, Parham

    2008-08-01

    In this research an intelligent emotional learning controller, Takagi- Sugeno- Kang (TSK) is applied to govern the dynamics of a novel Ionic-Polymer Metal Composite (IPMC) actuated manipulator. Ionic-Polymer Metal Composites are active actuators that show very large deformation in existence of low applied voltage. In this research, a new IPMC actuator is considered and applied to a 2-dof miniature manipulator. This manipulator is designed for miniature tasks. The control system consists of a set of neurofuzzy controller whose parameters are adapted according to the emotional learning rules, and a critic with task to assess the present situation resulted from the applied control action in terms of satisfactory achievement of the control goals and provides the emotional signal (the stress). The controller modifies its characteristics so that the critic's stress decreased.

  16. E-Learning System for Learning Virtual Circuit Making with a Microcontroller and Programming to Control a Robot

    ERIC Educational Resources Information Center

    Takemura, Atsushi

    2015-01-01

    This paper proposes a novel e-Learning system for learning electronic circuit making and programming a microcontroller to control a robot. The proposed e-Learning system comprises a virtual-circuit-making function for the construction of circuits with a versatile, Arduino microcontroller and an educational system that can simulate behaviors of…

  17. Optimal control in microgrid using multi-agent reinforcement learning.

    PubMed

    Li, Fu-Dong; Wu, Min; He, Yong; Chen, Xin

    2012-11-01

    This paper presents an improved reinforcement learning method to minimize electricity costs on the premise of satisfying the power balance and generation limit of units in a microgrid with grid-connected mode. Firstly, the microgrid control requirements are analyzed and the objective function of optimal control for microgrid is proposed. Then, a state variable "Average Electricity Price Trend" which is used to express the most possible transitions of the system is developed so as to reduce the complexity and randomicity of the microgrid, and a multi-agent architecture including agents, state variables, action variables and reward function is formulated. Furthermore, dynamic hierarchical reinforcement learning, based on change rate of key state variable, is established to carry out optimal policy exploration. The analysis shows that the proposed method is beneficial to handle the problem of "curse of dimensionality" and speed up learning in the unknown large-scale world. Finally, the simulation results under JADE (Java Agent Development Framework) demonstrate the validity of the presented method in optimal control for a microgrid with grid-connected mode.

  18. Learning a Novel Myoelectric-Controlled Interface Task

    PubMed Central

    Radhakrishnan, Saritha M.; Baker, Stuart N.; Jackson, Andrew

    2008-01-01

    Control of myoelectric prostheses and brain–machine interfaces requires learning abstract neuromotor transformations. To investigate the mechanisms underlying this ability, we trained subjects to move a two-dimensional cursor using a myoelectric-controlled interface. With the upper limb immobilized, an electromyogram from multiple hand and arm muscles moved the cursor in directions that were either intuitive or nonintuitive and with high or low variability. We found that subjects could learn even nonintuitive arrangements to a high level of performance. Muscle-tuning functions were cosine shaped and modulated so as to reduce cursor variability. Subjects exhibited an additional preference for using hand muscles over arm muscles, which resulted from a greater capacity of these to form novel, task-specific synergies. In a second experiment, nonvisual feedback from the hand was degraded with amplitude- and frequency-modulated vibration. Although vibration impaired task performance, it did not affect the rate at which learning occurred. We therefore conclude that the motor system can acquire internal models of novel, abstract neuromotor mappings even in the absence of overt movements or accurate proprioceptive signals, but that the distal motor system may be better suited to provide flexible control signals for neuromotor prostheses than structures related to the arm. PMID:18667540

  19. Learning to push and learning to move: the adaptive control of contact forces

    PubMed Central

    Casadio, Maura; Pressman, Assaf; Mussa-Ivaldi, Ferdinando A.

    2015-01-01

    To be successful at manipulating objects one needs to apply simultaneously well controlled movements and contact forces. We present a computational theory of how the brain may successfully generate a vast spectrum of interactive behaviors by combining two independent processes. One process is competent to control movements in free space and the other is competent to control contact forces against rigid constraints. Free space and rigid constraints are singularities at the boundaries of a continuum of mechanical impedance. Within this continuum, forces and motions occur in “compatible pairs” connected by the equations of Newtonian dynamics. The force applied to an object determines its motion. Conversely, inverse dynamics determine a unique force trajectory from a movement trajectory. In this perspective, we describe motor learning as a process leading to the discovery of compatible force/motion pairs. The learned compatible pairs constitute a local representation of the environment's mechanics. Experiments on force field adaptation have already provided us with evidence that the brain is able to predict and compensate the forces encountered when one is attempting to generate a motion. Here, we tested the theory in the dual case, i.e., when one attempts at applying a desired contact force against a simulated rigid surface. If the surface becomes unexpectedly compliant, the contact point moves as a function of the applied force and this causes the applied force to deviate from its desired value. We found that, through repeated attempts at generating the desired contact force, subjects discovered the unique compatible hand motion. When, after learning, the rigid contact was unexpectedly restored, subjects displayed after effects of learning, consistent with the concurrent operation of a motion control system and a force control system. Together, theory and experiment support a new and broader view of modularity in the coordinated control of forces and motions

  20. Learning to push and learning to move: the adaptive control of contact forces.

    PubMed

    Casadio, Maura; Pressman, Assaf; Mussa-Ivaldi, Ferdinando A

    2015-01-01

    To be successful at manipulating objects one needs to apply simultaneously well controlled movements and contact forces. We present a computational theory of how the brain may successfully generate a vast spectrum of interactive behaviors by combining two independent processes. One process is competent to control movements in free space and the other is competent to control contact forces against rigid constraints. Free space and rigid constraints are singularities at the boundaries of a continuum of mechanical impedance. Within this continuum, forces and motions occur in "compatible pairs" connected by the equations of Newtonian dynamics. The force applied to an object determines its motion. Conversely, inverse dynamics determine a unique force trajectory from a movement trajectory. In this perspective, we describe motor learning as a process leading to the discovery of compatible force/motion pairs. The learned compatible pairs constitute a local representation of the environment's mechanics. Experiments on force field adaptation have already provided us with evidence that the brain is able to predict and compensate the forces encountered when one is attempting to generate a motion. Here, we tested the theory in the dual case, i.e., when one attempts at applying a desired contact force against a simulated rigid surface. If the surface becomes unexpectedly compliant, the contact point moves as a function of the applied force and this causes the applied force to deviate from its desired value. We found that, through repeated attempts at generating the desired contact force, subjects discovered the unique compatible hand motion. When, after learning, the rigid contact was unexpectedly restored, subjects displayed after effects of learning, consistent with the concurrent operation of a motion control system and a force control system. Together, theory and experiment support a new and broader view of modularity in the coordinated control of forces and motions.

  1. Reversal Learning in Humans and Gerbils: Dynamic Control Network Facilitates Learning

    PubMed Central

    Jarvers, Christian; Brosch, Tobias; Brechmann, André; Woldeit, Marie L.; Schulz, Andreas L.; Ohl, Frank W.; Lommerzheim, Marcel; Neumann, Heiko

    2016-01-01

    Biologically plausible modeling of behavioral reinforcement learning tasks has seen great improvements over the past decades. Less work has been dedicated to tasks involving contingency reversals, i.e., tasks in which the original behavioral goal is reversed one or multiple times. The ability to adjust to such reversals is a key element of behavioral flexibility. Here, we investigate the neural mechanisms underlying contingency-reversal tasks. We first conduct experiments with humans and gerbils to demonstrate memory effects, including multiple reversals in which subjects (humans and animals) show a faster learning rate when a previously learned contingency re-appears. Motivated by recurrent mechanisms of learning and memory for object categories, we propose a network architecture which involves reinforcement learning to steer an orienting system that monitors the success in reward acquisition. We suggest that a model sensory system provides feature representations which are further processed by category-related subnetworks which constitute a neural analog of expert networks. Categories are selected dynamically in a competitive field and predict the expected reward. Learning occurs in sequentialized phases to selectively focus the weight adaptation to synapses in the hierarchical network and modulate their weight changes by a global modulator signal. The orienting subsystem itself learns to bias the competition in the presence of continuous monotonic reward accumulation. In case of sudden changes in the discrepancy of predicted and acquired reward the activated motor category can be switched. We suggest that this subsystem is composed of a hierarchically organized network of dis-inhibitory mechanisms, dubbed a dynamic control network (DCN), which resembles components of the basal ganglia. The DCN selectively activates an expert network, corresponding to the current behavioral strategy. The trace of the accumulated reward is monitored such that large sudden

  2. Reversal Learning in Humans and Gerbils: Dynamic Control Network Facilitates Learning.

    PubMed

    Jarvers, Christian; Brosch, Tobias; Brechmann, André; Woldeit, Marie L; Schulz, Andreas L; Ohl, Frank W; Lommerzheim, Marcel; Neumann, Heiko

    2016-01-01

    Biologically plausible modeling of behavioral reinforcement learning tasks has seen great improvements over the past decades. Less work has been dedicated to tasks involving contingency reversals, i.e., tasks in which the original behavioral goal is reversed one or multiple times. The ability to adjust to such reversals is a key element of behavioral flexibility. Here, we investigate the neural mechanisms underlying contingency-reversal tasks. We first conduct experiments with humans and gerbils to demonstrate memory effects, including multiple reversals in which subjects (humans and animals) show a faster learning rate when a previously learned contingency re-appears. Motivated by recurrent mechanisms of learning and memory for object categories, we propose a network architecture which involves reinforcement learning to steer an orienting system that monitors the success in reward acquisition. We suggest that a model sensory system provides feature representations which are further processed by category-related subnetworks which constitute a neural analog of expert networks. Categories are selected dynamically in a competitive field and predict the expected reward. Learning occurs in sequentialized phases to selectively focus the weight adaptation to synapses in the hierarchical network and modulate their weight changes by a global modulator signal. The orienting subsystem itself learns to bias the competition in the presence of continuous monotonic reward accumulation. In case of sudden changes in the discrepancy of predicted and acquired reward the activated motor category can be switched. We suggest that this subsystem is composed of a hierarchically organized network of dis-inhibitory mechanisms, dubbed a dynamic control network (DCN), which resembles components of the basal ganglia. The DCN selectively activates an expert network, corresponding to the current behavioral strategy. The trace of the accumulated reward is monitored such that large sudden

  3. Hilar GABAergic Interneuron Activity Controls Spatial Learning and Memory Retrieval

    PubMed Central

    Andrews-Zwilling, Yaisa; Gillespie, Anna K.; Kravitz, Alexxai V.; Nelson, Alexandra B.; Devidze, Nino; Lo, Iris; Yoon, Seo Yeon; Bien-Ly, Nga; Ring, Karen; Zwilling, Daniel; Potter, Gregory B.; Rubenstein, John L. R.; Kreitzer, Anatol C.; Huang, Yadong

    2012-01-01

    Background Although extensive research has demonstrated the importance of excitatory granule neurons in the dentate gyrus of the hippocampus in normal learning and memory and in the pathogenesis of amnesia in Alzheimer's disease (AD), the role of hilar GABAergic inhibitory interneurons, which control the granule neuron activity, remains unclear. Methodology and Principal Findings We explored the function of hilar GABAergic interneurons in spatial learning and memory by inhibiting their activity through Cre-dependent viral expression of enhanced halorhodopsin (eNpHR3.0)—a light-driven chloride pump. Hilar GABAergic interneuron-specific expression of eNpHR3.0 was achieved by bilaterally injecting adeno-associated virus containing a double-floxed inverted open-reading frame encoding eNpHR3.0 into the hilus of the dentate gyrus of mice expressing Cre recombinase under the control of an enhancer specific for GABAergic interneurons. In vitro and in vivo illumination with a yellow laser elicited inhibition of hilar GABAergic interneurons and consequent activation of dentate granule neurons, without affecting pyramidal neurons in the CA3 and CA1 regions of the hippocampus. We found that optogenetic inhibition of hilar GABAergic interneuron activity impaired spatial learning and memory retrieval, without affecting memory retention, as determined in the Morris water maze test. Importantly, optogenetic inhibition of hilar GABAergic interneuron activity did not alter short-term working memory, motor coordination, or exploratory activity. Conclusions and Significance Our findings establish a critical role for hilar GABAergic interneuron activity in controlling spatial learning and memory retrieval and provide evidence for the potential contribution of GABAergic interneuron impairment to the pathogenesis of amnesia in AD. PMID:22792368

  4. Applications of Adaptive Learning Controller to Synthetic Aperture Radar.

    DTIC Science & Technology

    1985-02-01

    FIGURE 37. Location of Two Sub- Phase Histories to be Utilized in Estimating Misfocus Coefficients A and C . . . A8 FIGURES 38.-94. ALC Learning Curves ...FIGURES (Concl uded) FIGURE 23. ALC Learning Curve .... .................. ... 45 .- " FIGURE 24. ALC Learning Curve ......... ................. 47 FIGURE...25. ALC Learning Curve .... .................. ... 48 FIGURE 26. ALC Learning Curve ....... .... ... .... 50 FIGURE 27. ALC Learning Curve

  5. Application of a calibrated tokamak transport model to ITER start-up study

    SciTech Connect

    Qiang, J.; Levinson, A.; Singer, C.E.

    1995-12-31

    A calibrated theory-based tokamak transport model is applied to ITER start-up studies. The reference simulation of basic ITER EDA parameters shows that a self sustained thermonuclear burn can be achieved provided that impurity control makes radiative losses sufficiently small. The ignition probabilities of both ITER EDA and CDA parameters are investigated. These results suggest that a significantly lower auxiliary heating power should heat ITER EDA to ignition.

  6. ITER (International Thermonuclear Experimental Reactor) current drive and heating physics

    SciTech Connect

    Nevins, W.M.; Lindquist, W. ); Fujisawa, N.; Kimura, H. ); Hopman, H.; Rebuffi, L.; Wegrowe, J.G. . NET Design Team); Parail, V.; Vdovin, V. . Inst. Atomnoj Ehn

    1990-01-01

    The ITER Current Drive and Heating (CD H) systems are required for: Ionization and current initiation; Non-inductive current ramp-up assist; Heating of the plasma; Steady-state operation with full non-inductive current drive; Current profile control; and Burn control by modulation of the auxiliary power. Steady-state current drive is the most demanding requirement, so this has driven the choice of the ITER current drive and heating systems.

  7. Iterative marker excision system.

    PubMed

    Myronovskyi, Maksym; Rosenkränzer, Birgit; Luzhetskyy, Andriy

    2014-05-01

    The deletions of large genomic DNA fragments and consecutive gene knockouts are prerequisites for the generation of organisms with improved properties. One of the key issues in this context is the removal of antibiotic resistance markers from engineered organisms without leaving an active recombinase recognition site. Here, we report the establishment of an iterative marker excision system (IMES) that solves this problem. Based on the phiC31 integrase and its mutant att sites, IMES can be used for highly effective deletion of DNA fragments between inversely oriented B-CC and P-GG sites. The B-CC and P-GG sites are derived from attB and attP by substitution of the central core TT dinucleotide with CC and GG, respectively. An unnatural RR site that resides in the chromosome following deletion is the joining product of the right shoulders of B-CC and P-GG. We show that the RR sites do not recombine with each other as well as the RR site recombines with B-CC. The recombination efficiencies between RR and P-GG or RR and LL are only 0.1 % and 1 %, respectively. Thus, IMES can be used for multistep genomic engineering without risking unwanted DNA recombination. The fabrication of multi-purpose antibiotic cassettes and examples of the utilisation of IMES are described.

  8. ECE Diagnostics for ITER

    NASA Astrophysics Data System (ADS)

    Ellis, Richard; Austin, Max; Beno, Joseph; Rowan, William; Phillips, Perry; Hubbard, Amanda; Pandya, Hitesh; Feder, Russel

    2013-10-01

    ECE on ITER will be used to measure electron temperature profiles and non thermal features of the distribution. The diagnostic has two systems, one radial, and the other viewing at a small oblique angle. Radiation will be conducted to the diagnostic area with large smooth wall waveguide. Emission will be measured with a multichannel Michelson interferometer and two microwave radiometers which cover the fundamental and second harmonic ECE (X and O mode). In-situ calibration employs a hot calibration source which has been designed, constructed, and tested. We report extensive wideband transmission measurements made on the DIII-D Michelson corrugated waveguide system. We have now completed design of the beam splitter box which separates X and O modes for both views. The box inputs are now located flush up against the vacuum windows on the port plug. We have then redesigned the Gaussian beam optics of the system to reduce the size of the calibration sources by 20% to allow a better fit with other diagnostics in the port plug. We will present the details of the entire new design.

  9. Beamforming and Power Control in Sensor Arrays Using Reinforcement Learning

    PubMed Central

    Almeida, Náthalee C.; Fernandes, Marcelo A.C.; Neto, Adrião D.D.

    2015-01-01

    The use of beamforming and power control, combined or separately, has advantages and disadvantages, depending on the application. The combined use of beamforming and power control has been shown to be highly effective in applications involving the suppression of interference signals from different sources. However, it is necessary to identify efficient methodologies for the combined operation of these two techniques. The most appropriate technique may be obtained by means of the implementation of an intelligent agent capable of making the best selection between beamforming and power control. The present paper proposes an algorithm using reinforcement learning (RL) to determine the optimal combination of beamforming and power control in sensor arrays. The RL algorithm used was Q-learning, employing an ε-greedy policy, and training was performed using the offline method. The simulations showed that RL was effective for implementation of a switching policy involving the different techniques, taking advantage of the positive characteristics of each technique in terms of signal reception. PMID:25808769

  10. An adaptive learning control system for large flexible structures

    NASA Technical Reports Server (NTRS)

    Thau, F. E.

    1985-01-01

    The objective of the research has been to study the design of adaptive/learning control systems for the control of large flexible structures. In the first activity an adaptive/learning control methodology for flexible space structures was investigated. The approach was based on using a modal model of the flexible structure dynamics and an output-error identification scheme to identify modal parameters. In the second activity, a least-squares identification scheme was proposed for estimating both modal parameters and modal-to-actuator and modal-to-sensor shape functions. The technique was applied to experimental data obtained from the NASA Langley beam experiment. In the third activity, a separable nonlinear least-squares approach was developed for estimating the number of excited modes, shape functions, modal parameters, and modal amplitude and velocity time functions for a flexible structure. In the final research activity, a dual-adaptive control strategy was developed for regulating the modal dynamics and identifying modal parameters of a flexible structure. A min-max approach was used for finding an input to provide modal parameter identification while not exceeding reasonable bounds on modal displacement.

  11. Intentional control based on familiarity in artificial grammar learning.

    PubMed

    Wan, Lulu; Dienes, Zoltán; Fu, Xiaolan

    2008-12-01

    It is commonly held that implicit learning is based largely on familiarity. It is also commonly held that familiarity is not affected by intentions. It follows that people should not be able to use familiarity to distinguish strings from two different implicitly learned grammars. In two experiments, subjects were trained on two grammars and then asked to endorse strings from only one of the grammars. Subjects also rated how familiar each string felt and reported whether or not they used familiarity to make their grammaticality judgment. We found subjects could endorse the strings of just one grammar and ignore the strings from the other. Importantly, when subjects said they were using familiarity, the rated familiarity for test strings consistent with their chosen grammar was greater than that for strings from the other grammar. Familiarity, subjectively defined, is sensitive to intentions and can play a key role in strategic control.

  12. Quantum Ensemble Classification: A Sampling-Based Learning Control Approach.

    PubMed

    Chen, Chunlin; Dong, Daoyi; Qi, Bo; Petersen, Ian R; Rabitz, Herschel

    2017-06-01

    Quantum ensemble classification (QEC) has significant applications in discrimination of atoms (or molecules), separation of isotopes, and quantum information extraction. However, quantum mechanics forbids deterministic discrimination among nonorthogonal states. The classification of inhomogeneous quantum ensembles is very challenging, since there exist variations in the parameters characterizing the members within different classes. In this paper, we recast QEC as a supervised quantum learning problem. A systematic classification methodology is presented by using a sampling-based learning control (SLC) approach for quantum discrimination. The classification task is accomplished via simultaneously steering members belonging to different classes to their corresponding target states (e.g., mutually orthogonal states). First, a new discrimination method is proposed for two similar quantum systems. Then, an SLC method is presented for QEC. Numerical results demonstrate the effectiveness of the proposed approach for the binary classification of two-level quantum ensembles and the multiclass classification of multilevel quantum ensembles.

  13. Universal learning network and its application to chaos control.

    PubMed

    Hirasawa, K; Wang, X; Murata, J; Hu, J; Jin, C

    2000-03-01

    Universal Learning Networks (ULNs) are proposed and their application to chaos control is discussed. ULNs provide a generalized framework to model and control complex systems. They consist of a number of inter-connected nodes where the nodes may have any continuously differentiable nonlinear functions in them and each pair of nodes can be connected by multiple branches with arbitrary time delays. Therefore, physical systems, which can be described by differential or difference equations and also their controllers, can be modeled in a unified way, and so ULNs may form a super set of neural networks and fuzzy neural networks. In order to optimize the ULNs, a generalized learning algorithm is derived, in which both the first order derivatives (gradients) and the higher order derivatives are incorporated. The derivatives are calculated by using forward or backward propagation schemes. These algorithms for calculating the derivatives are extended versions of Back Propagation Through Time (BPTT) and Real Time Recurrent Learning (RTRL) of Williams in the sense that generalized node functions, generalized network connections with multi-branch of arbitrary time delays, generalized criterion functions and higher order derivatives can be deal with. As an application of ULNs, a chaos control method using maximum Lyapunov exponent of ULNs is proposed. Maximum Lyapunov exponent of ULNs can be formulated by using higher order derivatives of ULNs, and the parameters of ULNs can be adjusted so that the maximum Lyapunov exponent approaches the target value. From the simulation results, it has been shown that a fully connected ULN with three nodes is able to display chaotic behaviors.

  14. Humans and Monkeys Exert Metacognitive Control Based on Learning Difficulty in a Perceptual Categorization Task

    ERIC Educational Resources Information Center

    Redford, Joshua S.

    2010-01-01

    Recently, Redford (2010) found that monkeys seemed to exert metacognitive control in a category-learning paradigm. Specifically, they selected more trials to view as the difficulty of the category-learning task increased. However, category-learning difficulty was determined by manipulating the family resemblance across the to-be-learned exemplars.…

  15. Machine learning of parameter control doctrine for sensor and communication systems. Final report

    SciTech Connect

    Kamen, R.B.; Dillard, R.A.

    1988-07-01

    Artificial-intelligence approaches to learning were reviewed for their potential contributions to the construction of a system to learn parameter-control doctrine. Separate learning tasks were isolated and several levels of related problems were distinguished. Formulas for providing the learning system with measures of its performance were derived for four kinds of targets.

  16. Effects of Dispositional Mindfulness on the Self-Controlled Learning of a Novel Motor Task

    ERIC Educational Resources Information Center

    Kee, Ying Hwa; Liu, Yeou-Teh

    2011-01-01

    Current literature suggests that mindful learning is beneficial to learning but its links with motor learning is seldom examined. In the present study, we examine the effects of learners' mindfulness disposition on the self-controlled learning of a novel motor task. Thirty-two participants undertook five practice sessions, in addition to a pre-,…

  17. Effects of Dispositional Mindfulness on the Self-Controlled Learning of a Novel Motor Task

    ERIC Educational Resources Information Center

    Kee, Ying Hwa; Liu, Yeou-Teh

    2011-01-01

    Current literature suggests that mindful learning is beneficial to learning but its links with motor learning is seldom examined. In the present study, we examine the effects of learners' mindfulness disposition on the self-controlled learning of a novel motor task. Thirty-two participants undertook five practice sessions, in addition to a pre-,…

  18. Modes of Executive Control in Sequence Learning: From Stimulus-Based to Plan-Based Control

    ERIC Educational Resources Information Center

    Tubau, Elisabet; Hommel, Bernhard; Lopez-Moliner, Joan

    2007-01-01

    The authors argue that human sequential learning is often but not always characterized by a shift from stimulus- to plan-based action control. To diagnose this shift, they manipulated the frequency of 1st-order transitions in a repeated manual left-right sequence, assuming that performance is sensitive to frequency-induced biases under stimulus-…

  19. Modes of Executive Control in Sequence Learning: From Stimulus-Based to Plan-Based Control

    ERIC Educational Resources Information Center

    Tubau, Elisabet; Hommel, Bernhard; Lopez-Moliner, Joan

    2007-01-01

    The authors argue that human sequential learning is often but not always characterized by a shift from stimulus- to plan-based action control. To diagnose this shift, they manipulated the frequency of 1st-order transitions in a repeated manual left-right sequence, assuming that performance is sensitive to frequency-induced biases under stimulus-…

  20. Model-based hierarchical reinforcement learning and human action control.

    PubMed

    Botvinick, Matthew; Weinstein, Ari

    2014-11-05

    Recent work has reawakened interest in goal-directed or 'model-based' choice, where decisions are based on prospective evaluation of potential action outcomes. Concurrently, there has been growing attention to the role of hierarchy in decision-making and action control. We focus here on the intersection between these two areas of interest, considering the topic of hierarchical model-based control. To characterize this form of action control, we draw on the computational framework of hierarchical reinforcement learning, using this to interpret recent empirical findings. The resulting picture reveals how hierarchical model-based mechanisms might play a special and pivotal role in human decision-making, dramatically extending the scope and complexity of human behaviour.

  1. Model-based hierarchical reinforcement learning and human action control

    PubMed Central

    Botvinick, Matthew; Weinstein, Ari

    2014-01-01

    Recent work has reawakened interest in goal-directed or ‘model-based’ choice, where decisions are based on prospective evaluation of potential action outcomes. Concurrently, there has been growing attention to the role of hierarchy in decision-making and action control. We focus here on the intersection between these two areas of interest, considering the topic of hierarchical model-based control. To characterize this form of action control, we draw on the computational framework of hierarchical reinforcement learning, using this to interpret recent empirical findings. The resulting picture reveals how hierarchical model-based mechanisms might play a special and pivotal role in human decision-making, dramatically extending the scope and complexity of human behaviour. PMID:25267822

  2. An Overview Of The ITER In-Vessel Coil Systems

    SciTech Connect

    Heitzenroeder, P J; Chrzanowski, J H; Dahlgren, F; Hawryluk, R J; Loesser, G D; Neumeyer, C; Mansfield, C; Smith, J P; Schaffer, M; Humphreys, D; Cordier, J J; Campbell, D; Johnson, G A; Martin, A; Rebut, P H; Tao, J O; Fogarty, P J; Nelson, B E; Reed, R P

    2009-09-24

    ELM mitigation is of particular importance in ITER in order to prevent rapid erosion or melting of the divertor surface, with the consequent risk of water leaks, increased plasma impurity content and disruptivity. Exploitable "natural" small or no ELM regimes might yet be found which extrapolate to ITER but this cannot be depended upon. Resonant Magnetic Perturbation has been added to pellet pacing as a tool for ITER to mitigate ELMs. Both are required, since neither method is fully developed and much work remains to be done. In addition, in-vessel coils enable vertical stabilization and RWM control. For these reasons, in-vessel coils (IVCs) are being designed for ITER to provide control of Edge Localized Modes (ELMs) in addition to providing control of moderately unstable resistive wall modes (RWMs) and the vertical stability (VS) of the plasma.

  3. The first fusion reactor: ITER

    NASA Astrophysics Data System (ADS)

    Campbell, D. J.

    2016-11-01

    Established by the signature of the ITER Agreement in November 2006 and currently under construction at St Paul-lez-Durance in southern France, the ITER project [1,2] involves the European Union (including Switzerland), China, India, Japan, the Russian Federation, South Korea and the United States. ITER (`the way' in Latin) is a critical step in the development of fusion energy. Its role is to provide an integrated demonstration of the physics and technology required for a fusion power plant based on magnetic confinement.

  4. Iterative free-energy optimization for recurrent neural networks (INFERNO)

    PubMed Central

    2017-01-01

    The intra-parietal lobe coupled with the Basal Ganglia forms a working memory that demonstrates strong planning capabilities for generating robust yet flexible neuronal sequences. Neurocomputational models however, often fails to control long range neural synchrony in recurrent spiking networks due to spontaneous activity. As a novel framework based on the free-energy principle, we propose to see the problem of spikes’ synchrony as an optimization problem of the neurons sub-threshold activity for the generation of long neuronal chains. Using a stochastic gradient descent, a reinforcement signal (presumably dopaminergic) evaluates the quality of one input vector to move the recurrent neural network to a desired activity; depending on the error made, this input vector is strengthened to hill-climb the gradient or elicited to search for another solution. This vector can be learned then by one associative memory as a model of the basal-ganglia to control the recurrent neural network. Experiments on habit learning and on sequence retrieving demonstrate the capabilities of the dual system to generate very long and precise spatio-temporal sequences, above two hundred iterations. Its features are applied then to the sequential planning of arm movements. In line with neurobiological theories, we discuss its relevance for modeling the cortico-basal working memory to initiate flexible goal-directed neuronal chains of causation and its relation to novel architectures such as Deep Networks, Neural Turing Machines and the Free-Energy Principle. PMID:28282439

  5. Iterative free-energy optimization for recurrent neural networks (INFERNO).

    PubMed

    Pitti, Alexandre; Gaussier, Philippe; Quoy, Mathias

    2017-01-01

    The intra-parietal lobe coupled with the Basal Ganglia forms a working memory that demonstrates strong planning capabilities for generating robust yet flexible neuronal sequences. Neurocomputational models however, often fails to control long range neural synchrony in recurrent spiking networks due to spontaneous activity. As a novel framework based on the free-energy principle, we propose to see the problem of spikes' synchrony as an optimization problem of the neurons sub-threshold activity for the generation of long neuronal chains. Using a stochastic gradient descent, a reinforcement signal (presumably dopaminergic) evaluates the quality of one input vector to move the recurrent neural network to a desired activity; depending on the error made, this input vector is strengthened to hill-climb the gradient or elicited to search for another solution. This vector can be learned then by one associative memory as a model of the basal-ganglia to control the recurrent neural network. Experiments on habit learning and on sequence retrieving demonstrate the capabilities of the dual system to generate very long and precise spatio-temporal sequences, above two hundred iterations. Its features are applied then to the sequential planning of arm movements. In line with neurobiological theories, we discuss its relevance for modeling the cortico-basal working memory to initiate flexible goal-directed neuronal chains of causation and its relation to novel architectures such as Deep Networks, Neural Turing Machines and the Free-Energy Principle.

  6. Cubic B-spline solution for two-point boundary value problem with AOR iterative method

    NASA Astrophysics Data System (ADS)

    Suardi, M. N.; Radzuan, N. Z. F. M.; Sulaiman, J.

    2017-09-01

    In this study, the cubic B-spline approximation equation has been derived by using the cubic B-spline discretization scheme to solve two-point boundary value problems. In addition to that, system of cubic B-spline approximation equations is generated from this spline approximation equation in order to get the numerical solutions. To do this, the Accelerated Over Relaxation (AOR) iterative method has been used to solve the generated linear system. For the purpose of comparison, the GS iterative method is designated as a control method to compare between SOR and AOR iterative methods. There are two examples of proposed problems that have been considered to examine the efficiency of these proposed iterative methods via three parameters such as their number of iterations, computational time and maximum absolute error. The numerical results are obtained from these iterative methods, it can be concluded that the AOR iterative method is slightly efficient as compared with SOR iterative method.

  7. Projection learning algorithm for threshold - controlled neural networks

    SciTech Connect

    Reznik, A.M.

    1995-03-01

    The projection learning algorithm proposed in [1, 2] and further developed in [3] substantially improves the efficiency of memorizing information and accelerates the learning process in neural networks. This algorithm is compatible with the completely connected neural network architecture (the Hopfield network [4]), but its application to other networks involves a number of difficulties. The main difficulties include constraints on interconnection structure and the need to eliminate the state uncertainty of latent neurons if such are present in the network. Despite the encouraging preliminary results of [3], further extension of the applications of the projection algorithm therefore remains problematic. In this paper, which is a continuation of the work begun in [3], we consider threshold-controlled neural networks. Networks of this type are quite common. They represent the receptor neuron layers in some neurocomputer designs. A similar structure is observed in the lower divisions of biological sensory systems [5]. In multilayer projection neural networks with lateral interconnections, the neuron layers or parts of these layers may also have the structure of a threshold-controlled completely connected network. Here the thresholds are the potentials delivered through the projection connections from other parts of the network. The extension of the projection algorithm to the class of threshold-controlled networks may accordingly prove to be useful both for extending its technical applications and for better understanding of the operation of the nervous system in living organisms.

  8. Memory and cognitive control circuits in mathematical cognition and learning.

    PubMed

    Menon, V

    2016-01-01

    Numerical cognition relies on interactions within and between multiple functional brain systems, including those subserving quantity processing, working memory, declarative memory, and cognitive control. This chapter describes recent advances in our understanding of memory and control circuits in mathematical cognition and learning. The working memory system involves multiple parietal-frontal circuits which create short-term representations that allow manipulation of discrete quantities over several seconds. In contrast, hippocampal-frontal circuits underlying the declarative memory system play an important role in formation of associative memories and binding of new and old information, leading to the formation of long-term memories that allow generalization beyond individual problem attributes. The flow of information across these systems is regulated by flexible cognitive control systems which facilitate the integration and manipulation of quantity and mnemonic information. The implications of recent research for formulating a more comprehensive systems neuroscience view of the neural basis of mathematical learning and knowledge acquisition in both children and adults are discussed. © 2016 Elsevier B.V. All rights reserved.

  9. Thalamic control of human attention driven by memory and learning.

    PubMed

    de Bourbon-Teles, José; Bentley, Paul; Koshino, Saori; Shah, Kushal; Dutta, Agneish; Malhotra, Paresh; Egner, Tobias; Husain, Masud; Soto, David

    2014-05-05

    The role of the thalamus in high-level cognition-attention, working memory (WM), rule-based learning, and decision making-remains poorly understood, especially in comparison to that of cortical frontoparietal networks [1-3]. Studies of visual thalamus have revealed important roles for pulvinar and lateral geniculate nucleus in visuospatial perception and attention [4-10] and for mediodorsal thalamus in oculomotor control [11]. Ventrolateral thalamus contains subdivisions devoted to action control as part of a circuit involving the basal ganglia [12, 13] and motor, premotor, and prefrontal cortices [14], whereas anterior thalamus forms a memory network in connection with the hippocampus [15]. This connectivity profile suggests that ventrolateral and anterior thalamus may represent a nexus between mnemonic and control functions, such as action or attentional selection. Here, we characterize the role of thalamus in the interplay between memory and visual attention. We show that ventrolateral lesions impair the influence of WM representations on attentional deployment. A subsequent fMRI study in healthy volunteers demonstrates involvement of ventrolateral and, notably, anterior thalamus in biasing attention through WM contents. To further characterize the memory types used by the thalamus to bias attention, we performed a second fMRI study that involved learning of stimulus-stimulus associations and their retrieval from long-term memory to optimize attention in search. Responses in ventrolateral and anterior thalamic nuclei tracked learning of the predictiveness of these abstract associations and their use in directing attention. These findings demonstrate a key role for human thalamus in higher-level cognition, notably, in mnemonic biasing of attention.

  10. Patients with Parkinson's disease learn to control complex systems via procedural as well as non-procedural learning.

    PubMed

    Osman, Magda; Wilkinson, Leonora; Beigi, Mazda; Castaneda, Cristina Sanchez; Jahanshahi, Marjan

    2008-01-01

    The striatum is considered to mediate some forms of procedural learning. Complex dynamic control (CDC) tasks involve an individual having to make a series of sequential decisions to achieve a specific outcome (e.g. learning to operate and control a car), and they involve procedural learning. The aim of this study was to test the hypothesis that patients with Parkinson's disease who have striatal dysfunction, are impaired on CDC tasks only when learning involves procedural learning. 26 patients with Parkinson's disease (PD) and 26 age-matched controls performed two CDC tasks, one in which training was observation-based (non-procedural), and a second in which training was action-based (procedural). Both groups were able to control the system to a specific criterion equally well, regardless of the training condition. However, when reporting their knowledge of the underlying structure of the system, both groups showed poorer accuracy when learning took place through observation-based compared with action-based training. Moreover, the controls' accuracy in reporting the underlying structure of the systems was superior to that of PD patients. The findings suggest that the striatal dysfunction in Parkinson's disease is not associated with impairment of procedural learning, regardless of whether the task involved procedural learning or not. It is possible that the learning and performance on CDC tasks are mediated by perceptual priming mechanisms in the neocortex.

  11. Comparative study of a learning fuzzy PID controller and a self-tuning controller.

    PubMed

    Kazemian, H B

    2001-01-01

    The self-organising fuzzy controller is an extension of the rule-based fuzzy controller with an additional learning capability. The self-organising fuzzy (SOF) is used as a master controller to readjust conventional PID gains at the actuator level during the system operation, copying the experience of a human operator. The application of the self-organising fuzzy PID (SOF-PID) controller to a 2-link non-linear revolute-joint robot-arm is studied using path tracking trajectories at the setpoint. For the purpose of comparison, the same experiments are repeated by using the self-tuning controller subject to the same data supplied at the setpoint. For the path tracking experiments, the output trajectories of the SOF-PID controller followed the specified path closer and smoother than the self-tuning controller.

  12. Relationship of field dependence/independence with learning styles and locus of control among registered nurses.

    PubMed

    Murphy, P H

    1993-06-01

    This study investigated the relations among scores on field dependence/independence, learning styles, and locus of control for 199 Registered Nurses. Hypotheses were that nurses with higher scores on field independence would score higher on internal locus of control and nurses with scores on concrete learning styles would score higher on field independence and internal locus of control. The Group Embedded Figures Test, Learning Style Inventory, and the Internal-External Scale, and a demographic questionnaire were administered. Analysis showed that nurses were field dependent, used the Reflective Observation mode of learning, displayed abstract and active learning styles, and scored as internal on the measure of locus of control.

  13. Motor control alteration in posturography in learning-disabled children.

    PubMed

    Poblano, Adrián; Ishiwara, Kioko; de Lourdes Arias, Ma; García-Pedroza, Felipe; Marín, Hildegarda; Trujillo, Marla

    2002-01-01

    Some authors have mentioned that intersensory integration is damaged in children with learning disabilities (LDs), and other investigators point to motor control alterations in the same patients. Thus, we decided to study these hypotheses by means of posturographic recordings. A highly selected group of 27 children with LDs was compared with 27 children of control group without LDs. Patients and controls were placed on the Equitest equipment platform. Sensory organization tests evaluated different test conditions that systematically vary visual, vestibular, and foot somatosensory cues available to subjects while they attempt to maintain a stable, quiet stance. Movement coordination test involved sudden posterior and anterior translations of the patient support surface. No significant correlations between scholastics and posturographic performance were observed. No difference in the six conditions and in sensory organization ratios or in visual preference between both groups was disclosed. Motor control test on children with LDs showed significant higher values in latencies in averages of large translations. Our data suggest that vestibular-visual-somatosensory organization for posture control are not abnormal in children with LDs; instead, motor controls show higher latencies with large translation movements, which suggest abnormal rate and timing precision motor coordination.

  14. Lessons learned on the Ground Test Accelerator control system

    SciTech Connect

    Kozubal, A.J.; Weiss, R.E.

    1994-09-01

    When we initiated the control system design for the Ground Test Accelerator (GTA), we envisioned a system that would be flexible enough to handle the changing requirements of an experimental project. This control system would use a developers` toolkit to reduce the cost and time to develop applications for GTA, and through the use of open standards, the system would accommodate unforeseen requirements as they arose. Furthermore, we would attempt to demonstrate on GTA a level of automation far beyond that achieved by existing accelerator control systems. How well did we achieve these goals? What were the stumbling blocks to deploying the control system, and what assumptions did we make about requirements that turned out to be incorrect? In this paper we look at the process of developing a control system that evolved into what is now the ``Experimental Physics and Industrial Control System`` (EPICS). Also, we assess the impact of this system on the GTA project, as well as the impact of GTA on EPICS. The lessons learned on GTA will be valuable for future projects.

  15. Robot cognitive control with a neurophysiologically inspired reinforcement learning model.

    PubMed

    Khamassi, Mehdi; Lallée, Stéphane; Enel, Pierre; Procyk, Emmanuel; Dominey, Peter F

    2011-01-01

    A major challenge in modern robotics is to liberate robots from controlled industrial settings, and allow them to interact with humans and changing environments in the real-world. The current research attempts to determine if a neurophysiologically motivated model of cortical function in the primate can help to address this challenge. Primates are endowed with cognitive systems that allow them to maximize the feedback from their environment by learning the values of actions in diverse situations and by adjusting their behavioral parameters (i.e., cognitive control) to accommodate unexpected events. In such contexts uncertainty can arise from at least two distinct sources - expected uncertainty resulting from noise during sensory-motor interaction in a known context, and unexpected uncertainty resulting from the changing probabilistic structure of the environment. However, it is not clear how neurophysiological mechanisms of reinforcement learning and cognitive control integrate in the brain to produce efficient behavior. Based on primate neuroanatomy and neurophysiology, we propose a novel computational model for the interaction between lateral prefrontal and anterior cingulate cortex reconciling previous models dedicated to these two functions. We deployed the model in two robots and demonstrate that, based on adaptive regulation of a meta-parameter β that controls the exploration rate, the model can robustly deal with the two kinds of uncertainties in the real-world. In addition the model could reproduce monkey behavioral performance and neurophysiological data in two problem-solving tasks. A last experiment extends this to human-robot interaction with the iCub humanoid, and novel sources of uncertainty corresponding to "cheating" by the human. The combined results provide concrete evidence for the ability of neurophysiologically inspired cognitive systems to control advanced robots in the real-world.

  16. Robot Cognitive Control with a Neurophysiologically Inspired Reinforcement Learning Model

    PubMed Central

    Khamassi, Mehdi; Lallée, Stéphane; Enel, Pierre; Procyk, Emmanuel; Dominey, Peter F.

    2011-01-01

    A major challenge in modern robotics is to liberate robots from controlled industrial settings, and allow them to interact with humans and changing environments in the real-world. The current research attempts to determine if a neurophysiologically motivated model of cortical function in the primate can help to address this challenge. Primates are endowed with cognitive systems that allow them to maximize the feedback from their environment by learning the values of actions in diverse situations and by adjusting their behavioral parameters (i.e., cognitive control) to accommodate unexpected events. In such contexts uncertainty can arise from at least two distinct sources – expected uncertainty resulting from noise during sensory-motor interaction in a known context, and unexpected uncertainty resulting from the changing probabilistic structure of the environment. However, it is not clear how neurophysiological mechanisms of reinforcement learning and cognitive control integrate in the brain to produce efficient behavior. Based on primate neuroanatomy and neurophysiology, we propose a novel computational model for the interaction between lateral prefrontal and anterior cingulate cortex reconciling previous models dedicated to these two functions. We deployed the model in two robots and demonstrate that, based on adaptive regulation of a meta-parameter β that controls the exploration rate, the model can robustly deal with the two kinds of uncertainties in the real-world. In addition the model could reproduce monkey behavioral performance and neurophysiological data in two problem-solving tasks. A last experiment extends this to human–robot interaction with the iCub humanoid, and novel sources of uncertainty corresponding to “cheating” by the human. The combined results provide concrete evidence for the ability of neurophysiologically inspired cognitive systems to control advanced robots in the real-world. PMID:21808619

  17. Effective learning strategies for real-time image-guided adaptive control of multiple-source hyperthermia applicators

    PubMed Central

    Cheng, Kung-Shan; Dewhirst, Mark W.; Stauffer, Paul R.; Das, Shiva

    2010-01-01

    Purpose: This paper investigates overall theoretical requirements for reducing the times required for the iterative learning of a real-time image-guided adaptive control routine for multiple-source heat applicators, as used in hyperthermia and thermal ablative therapy for cancer. Methods: Methods for partial reconstruction of the physical system with and without model reduction to find solutions within a clinically practical timeframe were analyzed. A mathematical analysis based on the Fredholm alternative theorem (FAT) was used to compactly analyze the existence and uniqueness of the optimal heating vector under two fundamental situations: (1) noiseless partial reconstruction and (2) noisy partial reconstruction. These results were coupled with a method for further acceleration of the solution using virtual source (VS) model reduction. The matrix approximation theorem (MAT) was used to choose the optimal vectors spanning the reduced-order subspace to reduce the time for system reconstruction and to determine the associated approximation error. Numerical simulations of the adaptive control of hyperthermia using VS were also performed to test the predictions derived from the theoretical analysis. A thigh sarcoma patient model surrounded by a ten-antenna phased-array applicator was retained for this purpose. The impacts of the convective cooling from blood flow and the presence of sudden increase of perfusion in muscle and tumor were also simulated. Results: By FAT, partial system reconstruction directly conducted in the full space of the physical variables such as phases and magnitudes of the heat sources cannot guarantee reconstructing the optimal system to determine the global optimal setting of the heat sources. A remedy for this limitation is to conduct the partial reconstruction within a reduced-order subspace spanned by the first few maximum eigenvectors of the true system matrix. By MAT, this VS subspace is the optimal one when the goal is to maximize the

  18. From Intent to Action: An Iterative Engineering Process

    ERIC Educational Resources Information Center

    Mouton, Patrice; Rodet, Jacques; Vacaresse, Sylvain

    2015-01-01

    Quite by chance, and over the course of a few haphazard meetings, a Master's degree in "E-learning Design" gradually developed in a Faculty of Economics. Its original and evolving design was the result of an iterative process carried out, not by a single Instructional Designer (ID), but by a full ID team. Over the last 10 years it has…

  19. The ITER project construction status

    NASA Astrophysics Data System (ADS)

    Motojima, O.

    2015-10-01

    The pace of the ITER project in St Paul-lez-Durance, France is accelerating rapidly into its peak construction phase. With the completion of the B2 slab in August 2014, which will support about 400 000 metric tons of the tokamak complex structures and components, the construction is advancing on a daily basis. Magnet, vacuum vessel, cryostat, thermal shield, first wall and divertor structures are under construction or in prototype phase in the ITER member states of China, Europe, India, Japan, Korea, Russia, and the United States. Each of these member states has its own domestic agency (DA) to manage their procurements of components for ITER. Plant systems engineering is being transformed to fully integrate the tokamak and its auxiliary systems in preparation for the assembly and operations phase. CODAC, diagnostics, and the three main heating and current drive systems are also progressing, including the construction of the neutral beam test facility building in Padua, Italy. The conceptual design of the Chinese test blanket module system for ITER has been completed and those of the EU are well under way. Significant progress has been made addressing several outstanding physics issues including disruption load characterization, prediction, avoidance, and mitigation, first wall and divertor shaping, edge pedestal and SOL plasma stability, fuelling and plasma behaviour during confinement transients and W impurity transport. Further development of the ITER Research Plan has included a definition of the required plant configuration for 1st plasma and subsequent phases of ITER operation as well as the major plasma commissioning activities and the needs of the accompanying R&D program to ITER construction by the ITER parties.

  20. Tailored, iterative, printed dietary feedback is as effective as group education in improving dietary behaviours: results from a randomised control trial in middle-aged adults with cardiovascular risk factors

    PubMed Central

    2011-01-01

    Background Tailored nutrition interventions have been shown to be more effective than non-tailored materials in changing dietary behaviours, particularly fat intake and fruit and vegetable intake. But further research examining efficacy of tailored nutrition education in comparison to other nutrition education methods and across a wider range of dietary behaviours is needed. The Stages to Healthy Eating Patterns Study (STEPs) was an intervention study, in middle-aged adults with cardiovascular risk factors, to examine the effectiveness of printed, tailored, iterative dietary feedback delivered by mail in improving short-term dietary behaviour in the areas of saturated fat, fruit, vegetable and grain and cereal intake. Methods STEPs was a 3-month randomised controlled trial with a pre and post-test design. There were three experimental conditions: 1) tailored, iterative, printed dietary feedback (TF) with three instalments mail-delivered over a 3-month period that were re-tailored to most recent assessment of dietary intake, intention to change and assessment of self-adequacy of dietary intake. Tailoring for dietary intake was performed on data from a validated 63-item combination FFQ designed for the purpose 2) small group nutrition education sessions (GE): consisting of two 90-minute dietitian-led small group nutrition education sessions and 3) and a wait-listed control (C) group who completed the dietary measures and socio-demographic questionnaires at baseline and 3-months later. Dietary outcome measures in the areas of saturated fat intake (g), and the intake of fruit (serves), vegetables (serves), grain and cereals as total and wholegrain (serves) were collected using 7-day estimated dietary records. Descriptive statistics, paired t-tests and general linear models adjusted for baseline dietary intake, age and gender were used to examine the effectiveness of different nutrition interventions. Results The TF group reported a significantly greater increase in

  1. A Reactive Blended Learning Proposal for an Introductory Control Engineering Course

    ERIC Educational Resources Information Center

    Mendez, Juan A.; Gonzalez, Evelio J.

    2010-01-01

    As it happens in other fields of engineering, blended learning is widely used to teach process control topics. In this paper, the inclusion of a reactive element--a Fuzzy Logic based controller--is proposed for a blended learning approach in an introductory control engineering course. This controller has been designed in order to regulate the…

  2. A Reactive Blended Learning Proposal for an Introductory Control Engineering Course

    ERIC Educational Resources Information Center

    Mendez, Juan A.; Gonzalez, Evelio J.

    2010-01-01

    As it happens in other fields of engineering, blended learning is widely used to teach process control topics. In this paper, the inclusion of a reactive element--a Fuzzy Logic based controller--is proposed for a blended learning approach in an introductory control engineering course. This controller has been designed in order to regulate the…

  3. Diverse power iteration embeddings: Theory and practice

    DOE PAGES

    Huang, Hao; Yoo, Shinjae; Yu, Dantong; ...

    2015-11-09

    Manifold learning, especially spectral embedding, is known as one of the most effective learning approaches on high dimensional data, but for real-world applications it raises a serious computational burden in constructing spectral embeddings for large datasets. To overcome this computational complexity, we propose a novel efficient embedding construction, Diverse Power Iteration Embedding (DPIE). DPIE shows almost the same effectiveness of spectral embeddings and yet is three order of magnitude faster than spectral embeddings computed from eigen-decomposition. Our DPIE is unique in that (1) it finds linearly independent embeddings and thus shows diverse aspects of dataset; (2) the proposed regularized DPIEmore » is effective if we need many embeddings; (3) we show how to efficiently orthogonalize DPIE if one needs; and (4) Diverse Power Iteration Value (DPIV) provides the importance of each DPIE like an eigen value. As a result, such various aspects of DPIE and DPIV ensure that our algorithm is easy to apply to various applications, and we also show the effectiveness and efficiency of DPIE on clustering, anomaly detection, and feature selection as our case studies.« less

  4. Magnetic fusion and project ITER

    SciTech Connect

    Park, H.K.

    1992-09-01

    It has already been demonstrated that our economics and international relationship are impacted by an energy crisis. For the continuing prosperity of the human race, a new and viable energy source must be developed within the next century. It is evident that the cost will be high and will require a long term commitment to achieve this goal due to a high degree of technological and scientific knowledge. Energy from the controlled nuclear fusion is a safe, competitive, and environmentally attractive but has not yet been completely conquered. Magnetic fusion is one of the most difficult technological challenges. In modem magnetic fusion devices, temperatures that are significantly higher than the temperatures of the sun have been achieved routinely and the successful generation of tens of million watts as a result of scientific break-even is expected from the deuterium and tritium experiment within the next few years. For the practical future fusion reactor, we need to develop reactor relevant materials and technologies. The international project called ``International Thermonuclear Experimental Reactor (ITER)`` will fulfill this need and the success of this project will provide the most attractive long-term energy source for mankind.

  5. Developing Conceptual Understanding and Procedural Skill in Mathematics: An Iterative Process.

    ERIC Educational Resources Information Center

    Rittle-Johnson, Bethany; Siegler, Robert S.; Alibali, Martha Wagner

    2001-01-01

    Proposes that conceptual and procedural knowledge develop in an iterative fashion and improved problem representation is one mechanism underlying the relations between them. Two experiments were conducted with 5th and 6th grade students learning about decimal fractions. Results indicate conceptual and procedural knowledge do develop, iteratively,…

  6. Hierarchical approximate policy iteration with binary-tree state space decomposition.

    PubMed

    Xu, Xin; Liu, Chunming; Yang, Simon X; Hu, Dewen

    2011-12-01

    In recent years, approximate policy iteration (API) has attracted increasing attention in reinforcement learning (RL), e.g., least-squares policy iteration (LSPI) and its kernelized version, the kernel-based LSPI algorithm. However, it remains difficult for API algorithms to obtain near-optimal policies for Markov decision processes (MDPs) with large or continuous state spaces. To address this problem, this paper presents a hierarchical API (HAPI) method with binary-tree state space decomposition for RL in a class of absorbing MDPs, which can be formulated as time-optimal learning control tasks. In the proposed method, after collecting samples adaptively in the state space of the original MDP, a learning-based decomposition strategy of sample sets was designed to implement the binary-tree state space decomposition process. Then, API algorithms were used on the sample subsets to approximate local optimal policies of sub-MDPs. The original MDP was decomposed into a binary-tree structure of absorbing sub-MDPs, constructed during the learning process, thus, local near-optimal policies were approximated by API algorithms with reduced complexity and higher precision. Furthermore, because of the improved quality of local policies, the combined global policy performed better than the near-optimal policy obtained by a single API algorithm in the original MDP. Three learning control problems, including path-tracking control of a real mobile robot, were studied to evaluate the performance of the HAPI method. With the same setting for basis function selection and sample collection, the proposed HAPI obtained better near-optimal policies than previous API methods such as LSPI and KLSPI.

  7. Machine Learning for Quantum Metrology and Quantum Control

    NASA Astrophysics Data System (ADS)

    Sanders, Barry; Zahedinejad, Ehsan; Palittapongarnpim, Pantita

    Generating quantum metrological procedures and quantum gate designs, subject to constraints such as temporal or particle-number bounds or limits on the number of control parameters, are typically hard computationally. Although greedy machine learning algorithms are ubiquitous for tackling these problems, the severe constraints listed above limit the efficacy of such approaches. Our aim is to devise heuristic machine learning techniques to generate tractable procedures for adaptive quantum metrology and quantum gate design. In particular we have modified differential evolution to generate adaptive interferometric-phase quantum metrology procedures for up to 100 photons including loss and noise, and we have generated policies for designing single-shot high-fidelity three-qubit gates in superconducting circuits by avoided level crossings. Although quantum metrology and quantum control are regarded as disparate, we have developed a unified framework for these two subjects, and this unification enables us to transfer insights and breakthroughs from one of the topics to the other. Thanks to NSERC, AITF and 1000 Talent Plan.

  8. Learning-based Nonlinear Model Predictive Control to Improve Vision-based Mobile Robot Path Tracking

    DTIC Science & Technology

    2015-07-01

    Learning -based Nonlinear Model Predictive Control to Improve Vision-based Mobile Robot Path Tracking Chris J. Ostafew Institute for Aerospace Studies...paper presents a Learning -based Nonlinear Model Predictive Control (LB-NMPC) algorithm to achieve high-performance path tracking in challenging off-road...terrain through learning . The LB-NMPC algorithm uses a simple a priori vehicle model and a learned disturbance model. Disturbances are modelled as a

  9. Algebraic and adaptive learning in neural control systems

    NASA Astrophysics Data System (ADS)

    Ferrari, Silvia

    A systematic approach is developed for designing adaptive and reconfigurable nonlinear control systems that are applicable to plants modeled by ordinary differential equations. The nonlinear controller comprising a network of neural networks is taught using a two-phase learning procedure realized through novel techniques for initialization, on-line training, and adaptive critic design. A critical observation is that the gradients of the functions defined by the neural networks must equal corresponding linear gain matrices at chosen operating points. On-line training is based on a dual heuristic adaptive critic architecture that improves control for large, coupled motions by accounting for actual plant dynamics and nonlinear effects. An action network computes the optimal control law; a critic network predicts the derivative of the cost-to-go with respect to the state. Both networks are algebraically initialized based on prior knowledge of satisfactory pointwise linear controllers and continue to adapt on line during full-scale simulations of the plant. On-line training takes place sequentially over discrete periods of time and involves several numerical procedures. A backpropagating algorithm called Resilient Backpropagation is modified and successfully implemented to meet these objectives, without excessive computational expense. This adaptive controller is as conservative as the linear designs and as effective as a global nonlinear controller. The method is successfully implemented for the full-envelope control of a six-degree-of-freedom aircraft simulation. The results show that the on-line adaptation brings about improved performance with respect to the initialization phase during aircraft maneuvers that involve large-angle and coupled dynamics, and parameter variations.

  10. Autonomy Supported, Learner-Controlled or System-Controlled Learning in Hypermedia Environments and the Influence of Academic Self-Regulation Style

    ERIC Educational Resources Information Center

    Gorissen, Chantal J. J.; Kester, Liesbeth; Brand-Gruwel, Saskia; Martens, Rob

    2015-01-01

    This study focuses on learning in three different hypermedia environments that either support autonomous learning, learner-controlled learning or system-controlled learning and explores the mediating role of academic self-regulation style (ASRS; i.e. a macro level of motivation) on learning. This research was performed to gain more insight in the…

  11. Autonomy Supported, Learner-Controlled or System-Controlled Learning in Hypermedia Environments and the Influence of Academic Self-Regulation Style

    ERIC Educational Resources Information Center

    Gorissen, Chantal J. J.; Kester, Liesbeth; Brand-Gruwel, Saskia; Martens, Rob

    2015-01-01

    This study focuses on learning in three different hypermedia environments that either support autonomous learning, learner-controlled learning or system-controlled learning and explores the mediating role of academic self-regulation style (ASRS; i.e. a macro level of motivation) on learning. This research was performed to gain more insight in the…

  12. Experiential learning in control systems laboratories and engineering project management

    NASA Astrophysics Data System (ADS)

    Reck, Rebecca Marie

    Experiential learning is a process by which a student creates knowledge through the insights gained from an experience. Kolb's model of experiential learning is a cycle of four modes: (1) concrete experience, (2) reflective observation, (3) abstract conceptualization, and (4) active experimentation. His model is used in each of the three studies presented in this dissertation. Laboratories are a popular way to apply the experiential learning modes in STEM courses. Laboratory kits allow students to take home laboratory equipment to complete experiments on their own time. Although students like laboratory kits, no previous studies compared student learning outcomes on assignments using laboratory kits with existing laboratory equipment. In this study, we examined the similarities and differences between the experiences of students who used a portable laboratory kit and students who used the traditional equipment. During the 2014- 2015 academic year, we conducted a quasi-experiment to compare students' achievement of learning outcomes and their experiences in the instructional laboratory for an introductory control systems course. Half of the laboratory sections in each semester used the existing equipment, while the other sections used a new kit. We collected both quantitative data and qualitative data. We did not identify any major differences in the student experience based on the equipment they used. Course objectives, like research objectives and product requirements, help provide clarity and direction for faculty and students. Unfortunately, course and laboratory objectives are not always clearly stated. Without a clear set of objectives, it can be hard to design a learning experience and determine whether students are achieving the intended outcomes of the course or laboratory. In this study, I identified a common set of laboratory objectives, concepts, and components of a laboratory apparatus for undergraduate control systems laboratories. During the summer of

  13. ITER Central Solenoid Module Fabrication

    SciTech Connect

    Smith, John

    2016-09-23

    The fabrication of the modules for the ITER Central Solenoid (CS) has started in a dedicated production facility located in Poway, California, USA. The necessary tools have been designed, built, installed, and tested in the facility to enable the start of production. The current schedule has first module fabrication completed in 2017, followed by testing and subsequent shipment to ITER. The Central Solenoid is a key component of the ITER tokamak providing the inductive voltage to initiate and sustain the plasma current and to position and shape the plasma. The design of the CS has been a collaborative effort between the US ITER Project Office (US ITER), the international ITER Organization (IO) and General Atomics (GA). GA’s responsibility includes: completing the fabrication design, developing and qualifying the fabrication processes and tools, and then completing the fabrication of the seven 110 tonne CS modules. The modules will be shipped separately to the ITER site, and then stacked and aligned in the Assembly Hall prior to insertion in the core of the ITER tokamak. A dedicated facility in Poway, California, USA has been established by GA to complete the fabrication of the seven modules. Infrastructure improvements included thick reinforced concrete floors, a diesel generator for backup power, along with, cranes for moving the tooling within the facility. The fabrication process for a single module requires approximately 22 months followed by five months of testing, which includes preliminary electrical testing followed by high current (48.5 kA) tests at 4.7K. The production of the seven modules is completed in a parallel fashion through ten process stations. The process stations have been designed and built with most stations having completed testing and qualification for carrying out the required fabrication processes. The final qualification step for each process station is achieved by the successful production of a prototype coil. Fabrication of the first

  14. The adaptive drop foot stimulator - Multivariable learning control of foot pitch and roll motion in paretic gait.

    PubMed

    Seel, Thomas; Werner, Cordula; Schauer, Thomas

    2016-11-01

    Many stroke patients suffer from the drop foot syndrome, which is characterized by a limited ability to lift (the lateral and/or medial edge of) the foot and leads to a pathological gait. In this contribution, we consider the treatment of this syndrome via functional electrical stimulation (FES) of the peroneal nerve during the swing phase of the paretic foot. A novel three-electrodes setup allows us to manipulate the recruitment of m. tibialis anterior and m. fibularis longus via two independent FES channels without violating the zero-net-current requirement of FES. We characterize the domain of admissible stimulation intensities that results from the nonlinearities in patients' stimulation intensity tolerance. To compensate most of the cross-couplings between the FES intensities and the foot motion, we apply a nonlinear controller output mapping. Gait phase transitions as well as foot pitch and roll angles are assessed in realtime by means of an Inertial Measurement Unit (IMU). A decentralized Iterative Learning Control (ILC) scheme is used to adjust the stimulation to the current needs of the individual patient. We evaluate the effectiveness of this approach in experimental trials with drop foot patients walking on a treadmill and on level ground. Starting from conventional stimulation parameters, the controller automatically determines individual stimulation parameters and thus achieves physiological foot pitch and roll angle trajectories within at most two strides. Copyright © 2016 IPEM. Published by Elsevier Ltd. All rights reserved.

  15. Reinforcement-learning-based output-feedback control of nonstrict nonlinear discrete-time systems with application to engine emission control.

    PubMed

    Shih, Peter; Kaul, Brian C; Jagannathan, Sarangapani; Drallmeier, James A

    2009-10-01

    A novel reinforcement-learning-based output adaptive neural network (NN) controller, which is also referred to as the adaptive-critic NN controller, is developed to deliver the desired tracking performance for a class of nonlinear discrete-time systems expressed in nonstrict feedback form in the presence of bounded and unknown disturbances. The adaptive-critic NN controller consists of an observer, a critic, and two action NNs. The observer estimates the states and output, and the two action NNs provide virtual and actual control inputs to the nonlinear discrete-time system. The critic approximates a certain strategic utility function, and the action NNs minimize the strategic utility function and control inputs. All NN weights adapt online toward minimization of a performance index, utilizing the gradient-descent-based rule, in contrast with iteration-based adaptive-critic schemes. Lyapunov functions are used to show the stability of the closed-loop tracking error, weights, and observer estimates. Separation and certainty equivalence principles, persistency of excitation condition, and linearity in the unknown parameter assumption are not needed. Experimental results on a spark ignition (SI) engine operating lean at an equivalence ratio of 0.75 show a significant (25%) reduction in cyclic dispersion in heat release with control, while the average fuel input changes by less than 1% compared with the uncontrolled case. Consequently, oxides of nitrogen (NO(x)) drop by 30%, and unburned hydrocarbons drop by 16% with control. Overall, NO(x)'s are reduced by over 80% compared with stoichiometric levels.

  16. Efficient exploration through active learning for value function approximation in reinforcement learning.

    PubMed

    Akiyama, Takayuki; Hachiya, Hirotaka; Sugiyama, Masashi

    2010-06-01

    Appropriately designing sampling policies is highly important for obtaining better control policies in reinforcement learning. In this paper, we first show that the least-squares policy iteration (LSPI) framework allows us to employ statistical active learning methods for linear regression. Then we propose a design method of good sampling policies for efficient exploration, which is particularly useful when the sampling cost of immediate rewards is high. The effectiveness of the proposed method, which we call active policy iteration (API), is demonstrated through simulations with a batting robot.

  17. A Robust Reinforcement Learning Control Design Method for Nonlinear System with Partially Unknown Structure

    NASA Astrophysics Data System (ADS)

    Nakano, Kazuhiro; Obayashi, Masanao; Kuremoto, Takashi; Kobayashi, Kunikazu

    We propose a robust control system which has robustness for disturbance and can deal with a nonlinear system with partially unknown structure by fusing reinforcement learning and robust control theory. First, we solved an optimal control problem without using unknown part of functions of the system, using neural network and the repetition learning of reinforcement learning algorithm. Second, we built the robust reinforcement learning control system which permits uncertainty and has robustness for disturbance by fusing the idea of H infinity control theory with above system.

  18. Progress in LHCD: a tool for advanced regimes on ITER

    NASA Astrophysics Data System (ADS)

    Tuccillo, A. A.; Barbato, E.; Bae, Y. S.; Becoulet, A.; Bernabei, S.; Bibet, P.; Calabrò, G.; Cardinali, A.; Castaldo, C.; Cesario, R.; Cho, M. H.; Cirant, S.; Crisanti, F.; Ekedahl, A.; Eriksson, L.-G.; Farina, D.; Giruzzi, G.; Goniche, M.; Granucci, G.; Ide, S.; Imbeaux, F.; Karttunen, S.; Litaudon, X.; Mailloux, J.; Mazon, D.; Mirizzi, F.; Moreau, D.; Nowak, S.; Namkung, W.; Panaccione, L.; Pericoli-Ridolfini, V.; Peysson, Y.; Petrzilka, V.; Podda, S.; Rantamaki, K.; Santini, F.; Saveliev, A.; Schneider, M.; Sozzi, C.; Suzuki, T.

    2005-12-01

    The recent success in coupling lower hybrid (LH) waves in high performance plasmas at JET together with the first demonstration on FTU of the coupling capability of the new passive active multijunction launcher removed major concerns on the possibility of using LH on ITER. LH exhibits the highest experimental current drive (CD) efficiency at low plasma temperature thus making it the natural candidate for off-axis CD on ITER where current profile control will help in maintaining burning performance on a long-time scale. We review recent LH results: long internal transport barrier obtained in JET with current profile sustained and controlled by LH acting under real time feedback together with first LH control of flat q-profile in a hybrid regime with Te ~ Ti. Minutes long fully non-inductive LH driven discharges on Tore Supra (TS). High CD efficiency with electron cyclotron in synergy with LH obtained in FTU and TS opening the possibility of interesting scenarii on ITER for MHD stabilization. Preliminary results of LH modelling for ITER are also reported. A brief overview of ITER LH system is reported together with some indication of new coming LH experiments, in particular KSTAR where CW klystrons at the foreseen ITER frequency of 5 GHz are being developed.

  19. RSRA/X-Wing flight control system development - Lessons learned

    NASA Technical Reports Server (NTRS)

    Corliss, Lloyd D.; Dunn, William R.; Morrison, Michael A.

    1989-01-01

    The X-Wing, in concept, marries the efficiencies of a helicopter and fixed wing aircraft through the use of a four-bladed wing/rotor that can be rotated or stopped in flight. The RSRA/X-Wing flight test program was a technology demonstration of this concept which, after three successful flights, was discontinued in late 1987. In spite of many technical challenges in this program, such as the use of circulation control, the fabrication of a large all-composite rotor, the development of an advanced, quadruplex digital flight control system, and the need for higher harmonic control, no major technical problems had been encountered at the time of the stop-work order. This paper addresses the issues of flight control system development and focuses on lessons learned. As with other such programs, software development was the most consuming issue. Other subjects of discussion include the problems of balancing program goals with technical goals, software- and hard-ware-related problems, safety issues, and system testing.

  20. Pupil Diameter May Reflect Motor Control and Learning.

    PubMed

    White, Olivier; French, Robert M

    2017-01-01

    Non-luminance-mediated changes in pupil diameter have been used since the first studies by Darwin in 1872 as indicators of clinical, cognitive, and arousal states. However, the relation between processes involved in motor control and changes in pupil diameter remains largely unknown. Twenty participants attempted to compensate random walks of a cursor with a computer mouse to restrain its trajectory within a target circle while the authors recorded their pupil diameters. Two conditions allowed the authors to experimentally manipulate the motor and cognitive components of the task. First, the step size of the cursor's random walk was either large or small leading to 2 task difficulties (difficult or easy). Second, they instructed participants to imagine controlling the cursor by moving the mouse, but without actually moving it (task modality: imagined movement or real movement condition). Task difficulty and modality allowed the authors to show that pupil diameters reflect processes involved in motor control and in the processing of feedback, respectively. Furthermore, the authors also demonstrate that motor learning can be quantified by pupil size. This noninvasive approach provides a promising method for investigating not only motor control, but also motor imagery, a research field of growing importance in sports and rehabilitation.

  1. Fast online learning of control regime transitions for adaptive robotic mobility

    NASA Astrophysics Data System (ADS)

    Yamauchi, Brian

    2012-06-01

    We introduce a new framework, Model Transition Control (MTC), that models robot control problems as sets of linear control regimes linked by nonlinear transitions, and a new learning algorithm, Dynamic Threshold Learning (DTL), that learns the boundaries of these control regimes in real-time. We demonstrate that DTL can learn to prevent understeer and oversteer while controlling a simulated high-speed vehicle. We also show that DTL can enable an iRobot PackBot to avoid rollover in rough terrain and to actively shift its center-of-gravity to maintain balance when climbing obstacles. In all cases, DTL is able to learn control regime boundaries in a few minutes, often with single-digit numbers of learning trials.

  2. Reinforcement learning control with approximation of time-dependent agent dynamics

    NASA Astrophysics Data System (ADS)

    Kirkpatrick, Kenton Conrad

    Reinforcement Learning has received a lot of attention over the years for systems ranging from static game playing to dynamic system control. Using Reinforcement Learning for control of dynamical systems provides the benefit of learning a control policy without needing a model of the dynamics. This opens the possibility of controlling systems for which the dynamics are unknown, but Reinforcement Learning methods like Q-learning do not explicitly account for time. In dynamical systems, time-dependent characteristics can have a significant effect on the control of the system, so it is necessary to account for system time dynamics while not having to rely on a predetermined model for the system. In this dissertation, algorithms are investigated for expanding the Q-learning algorithm to account for the learning of sampling rates and dynamics approximations. For determining a proper sampling rate, it is desired to find the largest sample time that still allows the learning agent to control the system to goal achievement. An algorithm called Sampled-Data Q-learning is introduced for determining both this sample time and the control policy associated with that sampling rate. Results show that the algorithm is capable of achieving a desired sampling rate that allows for system control while not sampling "as fast as possible". Determining an approximation of an agent's dynamics can be beneficial for the control of hierarchical multiagent systems by allowing a high-level supervisor to use the dynamics approximations for task allocation decisions. To this end, algorithms are investigated for learning first- and second-order dynamics approximations. These algorithms are respectively called First-Order Dynamics Learning and Second-Order Dynamics Learning. The dynamics learning algorithms are evaluated on several examples that show their capability to learn accurate approximations of state dynamics. All of these algorithms are then evaluated on hierarchical multiagent systems

  3. Fusion Power measurement at ITER

    SciTech Connect

    Bertalot, L.; Barnsley, R.; Krasilnikov, V.; Stott, P.; Suarez, A.; Vayakis, G.; Walsh, M.

    2015-07-01

    Nuclear fusion research aims to provide energy for the future in a sustainable way and the ITER project scope is to demonstrate the feasibility of nuclear fusion energy. ITER is a nuclear experimental reactor based on a large scale fusion plasma (tokamak type) device generating Deuterium - Tritium (DT) fusion reactions with emission of 14 MeV neutrons producing up to 700 MW fusion power. The measurement of fusion power, i.e. total neutron emissivity, will play an important role for achieving ITER goals, in particular the fusion gain factor Q related to the reactor performance. Particular attention is given also to the development of the neutron calibration strategy whose main scope is to achieve the required accuracy of 10% for the measurement of fusion power. Neutron Flux Monitors located in diagnostic ports and inside the vacuum vessel will measure ITER total neutron emissivity, expected to range from 1014 n/s in Deuterium - Deuterium (DD) plasmas up to almost 10{sup 21} n/s in DT plasmas. The neutron detection systems as well all other ITER diagnostics have to withstand high nuclear radiation and electromagnetic fields as well ultrahigh vacuum and thermal loads. (authors)

  4. Improved Adaptive-Reinforcement Learning Control for morphing unmanned air vehicles.

    PubMed

    Valasek, John; Doebbler, James; Tandale, Monish D; Meade, Andrew J

    2008-08-01

    This paper presents an improved Adaptive-Reinforcement Learning Control methodology for the problem of unmanned air vehicle morphing control. The reinforcement learning morphing control function that learns the optimal shape change policy is integrated with an adaptive dynamic inversion control trajectory tracking function. An episodic unsupervised learning simulation using the Q-learning method is developed to replace an earlier and less accurate Actor-Critic algorithm. Sequential Function Approximation, a Galerkin-based scattered data approximation scheme, replaces a K-Nearest Neighbors (KNN) method and is used to generalize the learning from previously experienced quantized states and actions to the continuous state-action space, all of which may not have been experienced before. The improved method showed smaller errors and improved learning of the optimal shape compared to the KNN.

  5. The Network Operations Control Center upgrade task: Lessons learned

    NASA Technical Reports Server (NTRS)

    Sherif, J. S.; Tran, T.-L.; Lee, S.

    1994-01-01

    This article synthesizes and describes the lessons learned from the Network Operations Control Center (NOCC) upgrade project, from the requirements phase through development and test and transfer. At the outset, the NOCC upgrade was being performed simultaneously with two other interfacing and dependent upgrades at the Signal Processing Center (SPC) and Ground Communications Facility (GCF), thereby adding a significant measure of complexity to the management and overall coordination of the development and transfer-to-operations (DTO) effort. Like other success stories, this project carried with it the traditional elements of top management support and exceptional dedication of cognizant personnel. Additionally, there were several NOCC-specific reasons for success, such as end-to-end system engineering, adoption of open-system architecture, thorough requirements management, and use of appropriate off-the-shelf technologies. On the other hand, there were several difficulties, such as ill-defined external interfaces, transition issues caused by new communications protocols, ambivalent use of two sets of policies and standards, and mistailoring of the new JPL management standard (due to the lack of practical guidelines). This article highlights the key lessons learned, as a means of constructive suggestions for the benefit of future projects.

  6. Language learning and control in monolinguals and bilinguals.

    PubMed

    Bartolotti, James; Marian, Viorica

    2012-08-01

    Parallel language activation in bilinguals leads to competition between languages. Experience managing this interference may aid novel language learning by improving the ability to suppress competition from known languages. To investigate the effect of bilingualism on the ability to control native-language interference, monolinguals and bilinguals were taught an artificial language designed to elicit between-language competition. Partial activation of interlingual competitors was assessed with eye-tracking and mouse-tracking during a word recognition task in the novel language. Eye-tracking results showed that monolinguals looked at competitors more than bilinguals, and for a longer duration of time. Mouse-tracking results showed that monolinguals' mouse movements were attracted to native-language competitors, whereas bilinguals overcame competitor interference by increasing the activation of target items. Results suggest that bilinguals manage cross-linguistic interference more effectively than monolinguals. We conclude that language interference can affect lexical retrieval, but bilingualism may reduce this interference by facilitating access to a newly learned language. Copyright © 2012 Cognitive Science Society, Inc.

  7. Perceptual learning and sensomotor flexibility: cortical plasticity under attentional control?

    PubMed Central

    Fahle, Manfred

    2008-01-01

    Recent research reveals long-lasting cortical plasticity of early sensory cortices even in adults. Sensory signals could be modified under top-down control if necessary quite early in order to optimize their signal-to-noise ratio, leading to ‘low level’ or ‘early’ perceptual learning (PL). For easy tasks, such elaborate top-down influences are usually not required, and learning is restricted to late selection of the appropriate signals on higher cortical levels, which seems easier and faster to achieve. But to reach the absolute limits of sensory performance, PL seems to optimize the entire chain of sensory processing. Hence, improvement for these extreme perceptual abilities is quite specific for a number of stimulus parameters, such as the position in the visual field and sometimes even the trained eye, reflecting the specificity of receptive fields in early sensory cortices. Early PL may be just one example—even if a very extensive one—of the mechanisms of neuronal plasticity and sensomotor flexibility that are constantly updating our sensomotor representations as a result of experience. As an illustration, this review contains some new experimental results on PL and sensory flexibility in the context of adaptation to multifocal intraocular lenses. PMID:18977730

  8. Language Learning and Control in Monolinguals and Bilinguals

    PubMed Central

    Bartolotti, James; Marian, Viorica

    2012-01-01

    Parallel language activation in bilinguals leads to competition between languages. Experience managing this interference may aid novel language learning by improving the ability to suppress competition from known languages. To investigate the effect of bilingualism on the ability to control native-language interference, monolinguals and bilinguals were taught an artificial language designed to elicit between-language competition. Partial activation of interlingual competitors was assessed with eye-tracking and mouse-tracking during a word recognition task in the novel language. Eye-tracking results showed that monolinguals looked at competitors more than bilinguals, and for a longer duration of time. Mouse-tracking results showed that monolinguals’ mouse-movements were attracted to native-language competitors, while bilinguals overcame competitor interference by increasing activation of target items. Results suggest that bilinguals manage cross-linguistic interference more effectively than monolinguals. We conclude that language interference can affect lexical retrieval, but bilingualism may reduce this interference by facilitating access to a newly-learned language. PMID:22462514

  9. Allowing Learners to Choose: Self-Controlled Practice Schedules for Learning Multiple Movement Patterns

    ERIC Educational Resources Information Center

    Wu, Will F. W.; Magill, Richard A.

    2011-01-01

    For this study, we investigated the effects of self-controlled practice on learning multiple motor skills. Thirty participants were randomly assigned to self-control or yoked conditions. Participants learned a three-keystroke pattern with three different relative time structures. Those in the self-control group chose one of three relative time…

  10. Allowing Learners to Choose: Self-Controlled Practice Schedules for Learning Multiple Movement Patterns

    ERIC Educational Resources Information Center

    Wu, Will F. W.; Magill, Richard A.

    2011-01-01

    For this study, we investigated the effects of self-controlled practice on learning multiple motor skills. Thirty participants were randomly assigned to self-control or yoked conditions. Participants learned a three-keystroke pattern with three different relative time structures. Those in the self-control group chose one of three relative time…

  11. Relaxation Criteria for Iterated Traffic Simulations

    NASA Astrophysics Data System (ADS)

    Kelly, Terence; Nagel, Kai

    Iterative transportation microsimulations adjust traveler route plans by iterating between a microsimulation and a route planner. At each iteration, the route planner adjusts individuals' route choices based on the preceding microsimulations. Empirically, this process yields good results, but it is usually unclear when to stop the iterative process when modeling real-world traffic. This paper investigates several criteria to judge relaxation of the iterative process, emphasizing criteria related to traveler decision-making.

  12. ITER Construction--Plant System Integration

    SciTech Connect

    Tada, E.; Matsuda, S.

    2009-02-19

    This brief paper introduces how the ITER will be built in the international collaboration. The ITER Organization plays a central role in constructing ITER and leading it into operation. Since most of the ITER components are to be provided in-kind from the member countries, integral project management should be scoped in advance of real work. Those include design, procurement, system assembly, testing, licensing and commissioning of ITER.

  13. Finite-approximation-error-based discrete-time iterative adaptive dynamic programming.

    PubMed

    Wei, Qinglai; Wang, Fei-Yue; Liu, Derong; Yang, Xiong

    2014-12-01

    In this paper, a new iterative adaptive dynamic programming (ADP) algorithm is developed to solve optimal control problems for infinite horizon discrete-time nonlinear systems with finite approximation errors. First, a new generalized value iteration algorithm of ADP is developed to make the iterative performance index function converge to the solution of the Hamilton-Jacobi-Bellman equation. The generalized value iteration algorithm permits an arbitrary positive semi-definite function to initialize it, which overcomes the disadvantage of traditional value iteration algorithms. When the iterative control law and iterative performance index function in each iteration cannot accurately be obtained, for the first time a new "design method of the convergence criteria" for the finite-approximation-error-based generalized value iteration algorithm is established. A suitable approximation error can be designed adaptively to make the iterative performance index function converge to a finite neighborhood of the optimal performance index function. Neural networks are used to implement the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the developed method.

  14. Nuclear Instrumentation and Control Cyber Testbed Considerations – Lessons Learned

    SciTech Connect

    Jonathan Gray; Robert Anderson; Julio G. Rodriguez; Cheol-Kwon Lee

    2014-08-01

    Abstract: Identifying and understanding digital instrumentation and control (I&C) cyber vulnerabilities within nuclear power plants and other nuclear facilities, is critical if nation states desire to operate nuclear facilities safely, reliably, and securely. In order to demonstrate objective evidence that cyber vulnerabilities have been adequately identified and mitigated, a testbed representing a facility’s critical nuclear equipment must be replicated. Idaho National Laboratory (INL) has built and operated similar testbeds for common critical infrastructure I&C for over ten years. This experience developing, operating, and maintaining an I&C testbed in support of research identifying cyber vulnerabilities has led the Korean Atomic Energy Research Institute of the Republic of Korea to solicit the experiences of INL to help mitigate problems early in the design, development, operation, and maintenance of a similar testbed. The following information will discuss I&C testbed lessons learned and the impact of these experiences to KAERI.

  15. Electron density measurements in the ITER fusion plasma

    NASA Astrophysics Data System (ADS)

    Watts, Christopher; Udintsev, Victor; Andrew, Philip; Vayakis, George; Van Zeeland, Michael; Brower, David; Feder, Russell; Mukhin, Eugene; Tolstyakov, Sergey

    2013-08-01

    The operation of ITER requires high-quality estimates of the plasma electron density over multiple regions in the plasma for plasma evaluation, plasma control and machine protection purposes. Although the density regimes of ITER are not very different from those of existing tokamaks (1018-1021 m-3), the severe conditions of the fusion plasma environment present particular challenges to implementing these density diagnostics. In this paper we present an overview of the array of ITER electron density diagnostics designed to measure over the entire ITER domain: plasma core, pedestal, edge, scrape-off layer and divertor. It will focus on the challenges faced in making these measurements, and the technical solutions of the current designs.

  16. Error Field Correction in ITER

    SciTech Connect

    Park, Jong-kyu; Boozer, Allen H.; Menard, Jonathan E.; Schaffer, Michael J.

    2008-05-22

    A new method for correcting magnetic field errors in the ITER tokamak is developed using the Ideal Perturbed Equilibrium Code (IPEC). The dominant external magnetic field for driving islands is shown to be localized to the outboard midplane for three ITER equilibria that represent the projected range of operational scenarios. The coupling matrices between the poloidal harmonics of the external magnetic perturbations and the resonant fields on the rational surfaces that drive islands are combined for different equilibria and used to determine an ordered list of the dominant errors in the external magnetic field. It is found that efficient and robust error field correction is possible with a fixed setting of the correction currents relative to the currents in the main coils across the range of ITER operating scenarios that was considered.

  17. Construction Safety Forecast for ITER

    SciTech Connect

    cadwallader, lee charles

    2006-11-01

    The International Thermonuclear Experimental Reactor (ITER) project is poised to begin its construction activity. This paper gives an estimate of construction safety as if the experiment was being built in the United States. This estimate of construction injuries and potential fatalities serves as a useful forecast of what can be expected for construction of such a major facility in any country. These data should be considered by the ITER International Team as it plans for safety during the construction phase. Based on average U.S. construction rates, ITER may expect a lost workday case rate of < 4.0 and a fatality count of 0.5 to 0.9 persons per year.

  18. ITER Disruption Mitigation System Design

    NASA Astrophysics Data System (ADS)

    Rasmussen, David; Lyttle, M. S.; Baylor, L. R.; Carmichael, J. R.; Caughman, J. B. O.; Combs, S. K.; Ericson, N. M.; Bull-Ezell, N. D.; Fehling, D. T.; Fisher, P. W.; Foust, C. R.; Ha, T.; Meitner, S. J.; Nycz, A.; Shoulders, J. M.; Smith, S. F.; Warmack, R. J.; Coburn, J. D.; Gebhart, T. E.; Fisher, J. T.; Reed, J. R.; Younkin, T. R.

    2015-11-01

    The disruption mitigation system for ITER is under design and will require injection of up to 10 kPa-m3 of deuterium, helium, neon, or argon material for thermal mitigation and up to 100 kPa-m3 of material for suppression of runaway electrons. A hybrid unit compatible with the ITER nuclear, thermal and magnetic field environment is being developed. The unit incorporates a fast gas valve for massive gas injection (MGI) and a shattered pellet injector (SPI) to inject a massive spray of small particles, and can be operated as an SPI with a frozen pellet or an MGI without a pellet. Three ITER upper port locations will have three SPI/MGI units with a common delivery tube. One equatorial port location has space for sixteen similar SPI/MGI units. Supported by US DOE under DE-AC05-00OR22725.

  19. A User-Centered Educational Modeling Language Improving the Controllability of Learning Design Quality

    ERIC Educational Resources Information Center

    Zendi, Asma; Bouhadada, Tahar; Bousbia, Nabila

    2016-01-01

    Semiformal EMLs are developed to facilitate the adoption of educational modeling languages (EMLs) and to address practitioners' learning design concerns, such as reusability and readability. In this article, SDLD (Structure Dialogue Learning Design) is presented, which is a semiformal EML that aims to improve controllability of learning design…

  20. Sustaining Teacher Control in a Blog-Based Personal Learning Environment

    ERIC Educational Resources Information Center

    Tomberg, Vladimir; Laanpere, Mart; Ley, Tobias; Normak, Peeter

    2013-01-01

    Various tools and services based on Web 2.0 (mainly blogs, wikis, social networking tools) are increasingly used in formal education to create personal learning environments, providing self-directed learners with more freedom, choice, and control over their learning. In such distributed and personalized learning environments, the traditional role…

  1. A User-Centered Educational Modeling Language Improving the Controllability of Learning Design Quality

    ERIC Educational Resources Information Center

    Zendi, Asma; Bouhadada, Tahar; Bousbia, Nabila

    2016-01-01

    Semiformal EMLs are developed to facilitate the adoption of educational modeling languages (EMLs) and to address practitioners' learning design concerns, such as reusability and readability. In this article, SDLD (Structure Dialogue Learning Design) is presented, which is a semiformal EML that aims to improve controllability of learning design…

  2. Cognitive Control over Learning: Creating, Clustering, and Generalizing Task-Set Structure

    ERIC Educational Resources Information Center

    Collins, Anne G. E.; Frank, Michael J.

    2013-01-01

    Learning and executive functions such as task-switching share common neural substrates, notably prefrontal cortex and basal ganglia. Understanding how they interact requires studying how cognitive control facilitates learning but also how learning provides the (potentially hidden) structure, such as abstract rules or task-sets, needed for…

  3. Cognitive Control over Learning: Creating, Clustering, and Generalizing Task-Set Structure

    ERIC Educational Resources Information Center

    Collins, Anne G. E.; Frank, Michael J.

    2013-01-01

    Learning and executive functions such as task-switching share common neural substrates, notably prefrontal cortex and basal ganglia. Understanding how they interact requires studying how cognitive control facilitates learning but also how learning provides the (potentially hidden) structure, such as abstract rules or task-sets, needed for…

  4. Machine learning control (MLC)---a novel method for optimal control of complex nonlinear systems

    NASA Astrophysics Data System (ADS)

    Noack, Bernd R.; Cordier, Laurent; Parezanovic, Vladimir; von Krbek, Kai; Segond, Marc; Abel, Markus W.; Brunton, Steven; Duriez, Thomas

    2014-11-01

    We propose a model-free closed-loop control strategy for complex nonlinear systems with a finite number of sensors and actuators (MIMO). This strategy yields a feedback law which optimizes a cost functional with machine learning methods. Thus, no dynamical model of the plant is required in contrast to model-based approaches, In addition, no working open-loop control is necessary in contrast to adaptive approaches. The approach is illustrated for strongly nonlinear dynamical systems which are not accessible to linear control design. Control studies of several shear-turbulence experiments will be presented in the talks of T. Duriez and V. Parezanović. Funding of the ANR Chair of Excellence TUCOROM, of the ANR Grant SepaCoDe, of the EC's Marie-Curie ITN program and of Ambrosys GmbH is acknowledged.

  5. Experimental Vertical Stability Studies for ITER Performance and Design Guidance

    SciTech Connect

    Humphreys, D A; Casper, T A; Eidietis, N; Ferrera, M; Gates, D A; Hutchinson, I H; Jackson, G L; Kolemen, E; Leuer, J A; Lister, J; LoDestro, L L; Meyer, W H; Pearlstein, L D; Sartori, F; Walker, M L; Welander, A S; Wolfe, S M

    2008-10-13

    Operating experimental devices have provided key inputs to the design process for ITER axisymmetric control. In particular, experiments have quantified controllability and robustness requirements in the presence of realistic noise and disturbance environments, which are difficult or impossible to characterize with modeling and simulation alone. This kind of information is particularly critical for ITER vertical control, which poses some of the highest demands on poloidal field system performance, since the consequences of loss of vertical control can be very severe. The present work describes results of multi-machine studies performed under a joint ITPA experiment on fundamental vertical control performance and controllability limits. We present experimental results from Alcator C-Mod, DIII-D, NSTX, TCV, and JET, along with analysis of these data to provide vertical control performance guidance to ITER. Useful metrics to quantify this control performance include the stability margin and maximum controllable vertical displacement. Theoretical analysis of the maximum controllable vertical displacement suggests effective approaches to improving performance in terms of this metric, with implications for ITER design modifications. Typical levels of noise in the vertical position measurement which can challenge the vertical control loop are assessed and analyzed.

  6. Iterative Restoration Of Tomosynthetic Slices

    NASA Astrophysics Data System (ADS)

    Ruttimann, U. E.; Groenhuis, R. A.; Webber, R. L.

    1984-08-01

    Tomosynthetic reconstructions suffer from the disadvantage that blurred images of object detail lying outside the plane of interest are superimposed over the desired image of structures in the tomosynthetic plane. It is proposed to selectively reduce these undesired superimpositions by a constrained iterative restoration method. Sufficient conditions are derived ensuring the convergence of the iterations to the exact solution in the absence of noise and constraints. Although in practice the restoration process must be left incomplete because of noise and quantization artifacts, the experimental results demonstrate that for reasons of stability these convergence conditions must be satisfied.

  7. Iterated binomial sums and their associated iterated integrals

    NASA Astrophysics Data System (ADS)

    Ablinger, J.; Blümlein, J.; Raab, C. G.; Schneider, C.

    2014-11-01

    We consider finite iterated generalized harmonic sums weighted by the binomial binom{2k}{k} in numerators and denominators. A large class of these functions emerges in the calculation of massive Feynman diagrams with local operator insertions starting at 3-loop order in the coupling constant and extends the classes of the nested harmonic, generalized harmonic, and cyclotomic sums. The binomially weighted sums are associated by the Mellin transform to iterated integrals over square-root valued alphabets. The values of the sums for N → ∞ and the iterated integrals at x = 1 lead to new constants, extending the set of special numbers given by the multiple zeta values, the cyclotomic zeta values and special constants which emerge in the limit N → ∞ of generalized harmonic sums. We develop algorithms to obtain the Mellin representations of these sums in a systematic way. They are of importance for the derivation of the asymptotic expansion of these sums and their analytic continuation to N in {C}. The associated convolution relations are derived for real parameters and can therefore be used in a wider context, as, e.g., for multi-scale processes. We also derive algorithms to transform iterated integrals over root-valued alphabets into binomial sums. Using generating functions we study a few aspects of infinite (inverse) binomial sums.

  8. Adaptive Control of Flat MIMO Nonlinear Systems with Additive Disturbance

    DTIC Science & Technology

    2006-01-01

    Survey of Iterative Learning Control,” IEEE Control Systems Magazine, Vol. 26, No. 3, pp. 96–114, 2006. [2] Z. Cai, M.S. de Queiroz , and D.M. Dawson...427, 1996. [17] B. Xian, D.M. Dawson, M.S. de Queiroz , and J. Chen, “A Continuous Asymptotic Tracking Control Strategy for Uncertain Nonlinear Sys- tems

  9. Online Adaptation and Over-Trial Learning in Macaque Visuomotor Control

    PubMed Central

    Braun, Daniel A.; Aertsen, Ad; Paz, Rony; Vaadia, Eilon; Rotter, Stefan; Mehring, Carsten

    2011-01-01

    When faced with unpredictable environments, the human motor system has been shown to develop optimized adaptation strategies that allow for online adaptation during the control process. Such online adaptation is to be contrasted to slower over-trial learning that corresponds to a trial-by-trial update of the movement plan. Here we investigate the interplay of both processes, i.e., online adaptation and over-trial learning, in a visuomotor experiment performed by macaques. We show that simple non-adaptive control schemes fail to perform in this task, but that a previously suggested adaptive optimal feedback control model can explain the observed behavior. We also show that over-trial learning as seen in learning and aftereffect curves can be explained by learning in a radial basis function network. Our results suggest that both the process of over-trial learning and the process of online adaptation are crucial to understand visuomotor learning. PMID:21720526

  10. iLab 20M: A Large-scale Controlled Object Dataset to Investigate Deep Learning

    DTIC Science & Technology

    2016-07-01

    iLab-20M: A large-scale controlled object dataset to investigate deep learning Ali Borji1 Saeed Izadi2 Laurent Itti3 1Center for Research in Computer...the emer- gence of highly popular deep learning models. While be- ing very useful for learning invariance to object inter- and intra-class shape...variability, these large-scale wild datasets are not very useful for learning invariance to other parame- ters urging researchers to resort to other tricks

  11. Towards Greater Learner Control: Web Supported Project-Based Learning

    ERIC Educational Resources Information Center

    Guthrie, Cameron

    2010-01-01

    Project-based learning has been suggested as an appropriate pedagogy to prepare students in information systems for the realities of the business world. Web-based resources have been used to support such pedagogy with mixed results. The paper argues that the design of web-based learning support to cater to different learning styles may give…

  12. Learning in the Digital Age: Control or Connection?

    ERIC Educational Resources Information Center

    Van Galen, Jane

    2013-01-01

    In October 2011, 200 state school officers and legislators gathered at a hotel in San Francisco to learn how to "revolutionize" learning by "personalizing" instruction. The occasion was former Florida Gov. Jeb Bush's second annual National Summit on Education Reform. The topic was digital learning. The vision of digitally managed curriculum and…

  13. Learning in the Digital Age: Control or Connection?

    ERIC Educational Resources Information Center

    Van Galen, Jane

    2013-01-01

    In October 2011, 200 state school officers and legislators gathered at a hotel in San Francisco to learn how to "revolutionize" learning by "personalizing" instruction. The occasion was former Florida Gov. Jeb Bush's second annual National Summit on Education Reform. The topic was digital learning. The vision of digitally managed curriculum and…

  14. Towards Greater Learner Control: Web Supported Project-Based Learning

    ERIC Educational Resources Information Center

    Guthrie, Cameron

    2010-01-01

    Project-based learning has been suggested as an appropriate pedagogy to prepare students in information systems for the realities of the business world. Web-based resources have been used to support such pedagogy with mixed results. The paper argues that the design of web-based learning support to cater to different learning styles may give…

  15. Motor control and learning with lower-limb myoelectric control in amputees.

    PubMed

    Alcaide-Aguirre, Ramses E; Morgenroth, David C; Ferris, Daniel P

    2013-01-01

    Advances in robotic technology have recently enabled the development of powered lower-limb prosthetic limbs. A major hurdle in developing commercially successful powered prostheses is the control interface. Myoelectric signals are one way for prosthetic users to provide feedforward volitional control of prosthesis mechanics. The goal of this study was to assess motor learning in people with lower-limb amputation using proportional myoelectric control from residual-limb muscles. We examined individuals with transtibial amputation and nondisabled controls performing tracking tasks of a virtual object. We assessed how quickly the individuals with amputation improved their performance and whether years since amputation correlated with performance. At the beginning of training, subjects with amputation performed much worse than control subjects. By the end of a short training period, tracking error did not significantly differ between subjects with amputation and nondisabled subjects. Initial but not final performance correlated significantly with time since amputation. This study demonstrates that although subjects with amputation may initially have poor volitional control of their residual lower-limb muscles, training can substantially improve their volitional control. These findings are encouraging for the future use of proportional myoelectric control of powered lower-limb prostheses.

  16. Information-educational environment with adaptive control of learning process

    NASA Astrophysics Data System (ADS)

    Modjaev, A. D.; Leonova, N. M.

    2017-01-01

    Recent years, a new scientific branch connected with the activities in social sphere management developing intensively and it is called "Social Cybernetics". In the framework of this scientific branch, theory and methods of management of social sphere are formed. Considerable attention is paid to the management, directly in real time. However, the decision of such management tasks is largely constrained by the lack of or insufficiently deep study of the relevant sections of the theory and methods of management. The article discusses the use of cybernetic principles in solving problems of control in social systems. Applying to educational activities a model of composite interrelated objects representing the behaviour of students at various stages of educational process is introduced. Statistical processing of experimental data obtained during the actual learning process is being done. If you increase the number of features used, additionally taking into account the degree and nature of variability of levels of current progress of students during various types of studies, new properties of students' grouping are discovered. L-clusters were identified, reflecting the behaviour of learners with similar characteristics during lectures. It was established that the characteristics of the clusters contain information about the dynamics of learners' behaviour, allowing them to be used in additional lessons. The ways of solving the problem of adaptive control based on the identified dynamic characteristics of the learners are planned.

  17. Active prospective control is required for effective sensorimotor learning.

    PubMed

    Snapp-Childs, Winona; Casserly, Elizabeth; Mon-Williams, Mark; Bingham, Geoffrey P

    2013-01-01

    Passive modeling of movements is often used in movement therapy to overcome disabilities caused by stroke or other disorders (e.g. Developmental Coordination Disorder or Cerebral Palsy). Either a therapist or, recently, a specially designed robot moves or guides the limb passively through the movement to be trained. In contrast, action theory has long suggested that effective skill acquisition requires movements to be actively generated. Is this true? In view of the former, we explicitly tested the latter. Previously, a method was developed that allows children with Developmental Coordination Disorder to produce effective movements actively, so as to improve manual performance to match that of typically developing children. In the current study, we tested practice using such active movements as compared to practice using passive movement. The passive movement employed, namely haptic tracking, provided a strong test of the comparison, one that showed that the mere inaction of the muscles is not the problem. Instead, lack of prospective control was. The result was no effective learning with passive movement while active practice with prospective control yielded significant improvements in performance.

  18. Gaussian Processes for Data-Efficient Learning in Robotics and Control.

    PubMed

    Deisenroth, Marc Peter; Fox, Dieter; Rasmussen, Carl Edward

    2015-02-01

    Autonomous learning has been a promising direction in control and robotics for more than a decade since data-driven learning allows to reduce the amount of engineering knowledge, which is otherwise required. However, autonomous reinforcement learning (RL) approaches typically require many interactions with the system to learn controllers, which is a practical limitation in real systems, such as robots, where many interactions can be impractical and time consuming. To address this problem, current learning approaches typically require task-specific knowledge in form of expert demonstrations, realistic simulators, pre-shaped policies, or specific knowledge about the underlying dynamics. In this paper, we follow a different approach and speed up learning by extracting more information from data. In particular, we learn a probabilistic, non-parametric Gaussian process transition model of the system. By explicitly incorporating model uncertainty into long-term planning and controller learning our approach reduces the effects of model errors, a key problem in model-based learning. Compared to state-of-the art RL our model-based policy search method achieves an unprecedented speed of learning. We demonstrate its applicability to autonomous learning in real robot and control tasks.

  19. Rule-based mechanisms of learning for intelligent adaptive flight control

    NASA Technical Reports Server (NTRS)

    Handelman, David A.; Stengel, Robert F.

    1990-01-01

    How certain aspects of human learning can be used to characterize learning in intelligent adaptive control systems is investigated. Reflexive and declarative memory and learning are described. It is shown that model-based systems-theoretic adaptive control methods exhibit attributes of reflexive learning, whereas the problem-solving capabilities of knowledge-based systems of artificial intelligence are naturally suited for implementing declarative learning. Issues related to learning in knowledge-based control systems are addressed, with particular attention given to rule-based systems. A mechanism for real-time rule-based knowledge acquisition is suggested, and utilization of this mechanism within the context of failure diagnosis for fault-tolerant flight control is demonstrated.

  20. Networking Theories by Iterative Unpacking

    ERIC Educational Resources Information Center

    Koichu, Boris

    2014-01-01

    An iterative unpacking strategy consists of sequencing empirically-based theoretical developments so that at each step of theorizing one theory serves as an overarching conceptual framework, in which another theory, either existing or emerging, is embedded in order to elaborate on the chosen element(s) of the overarching theory. The strategy is…